Recei ed Janua y 10, 2021, accep ed Janua y 15, 2021, da e o publica ion Janua y 22, 2021, da e o cu en e sion Feb ua y 1, 2021.
Digi al Objec Iden i ie 10.1109/ACCESS.2021.3053763
P edic ion o Ch onic Kidney Disease - A Machine
Lea ning Pe spec i e
PANKAJ CHITTORA 1, SANDEEP CHAURASIA1, (Senio Membe , IEEE),
PRASUN CHAKRABARTI2,3, (Senio Membe , IEEE), GAURAV KUMAWAT1,
TULIKA CHAKRABARTI4, ZBIGNIEW LEONOWICZ 5, (Senio Membe , IEEE),
MICHAŁ JASIŃSKI 5, (Membe , IEEE), ŁUKASZ JASIŃSKI 5,
RADOMIR GONO 6, (Senio Membe , IEEE), ELŻBIETA JASIŃSKA 7, AND VADIM BOLSHEV 8
1Depa men o Compu e Science and Enginee ing, Manipal Uni e si y Jaipu , Jaipu 303007, India
2Depa men o Compu e Science Enginee ing, Techno India NJR Ins i u e o Technology, Udaipu 313003, India
3Da a Analy ics and A i icial In elligence Labo a o y, Enginee ing-Technology School, Thu Dau Mo Uni e si y, Thu Dau Mo 820000, Vie nam
4Depa men o Basic Science (Chemis y), Si Padampa Singhania Uni e si y, Udaipu 3136022, India
5Depa men o Elec ical Enginee ing Fundamen als, Facul y o Elec ical Enginee ing, W oclaw Uni e si y o Science and Technology, 50-370 W oclaw, Poland
6Depa men o Elec ical Powe Enginee ing, Facul y o Elec ical Enginee ing and Compu e Science, VSB–Technical Uni e si y o Os a a, 708 00 Os a a,
Czech Republic
7Facul y o Law, Adminis a ion and Economics, Uni e si y o W oclaw, 50-145 W oclaw, Poland
8Labo a o y o Powe Supply and Hea Supply, Fede al Scien i ic Ag oenginee ing Cen e VIM, 109428 Moscow, Russia
Co esponding au ho : Michał Jasiński (michal.jasinski@pw .edu.pl)
This wo k was unded by he Chai o Elec ical Enginee ing Fundamen als (K38W05D02), W oclaw Uni e si y o Technology, W oclaw,
Poland.
ABSTRACT Ch onic Kidney Disease is one o he mos c i ical illness nowadays and p ope diagnosis is
equi ed as soon as possible. Machine lea ning echnique has become eliable o medical ea men . Wi h
he help o a machine lea ning classi ie algo i hms, he doc o can de ec he disease on ime. Fo his
pe spec i e, Ch onic Kidney Disease p edic ion has been discussed in his a icle. Ch onic Kidney Disease
da ase has been aken om he UCI eposi o y. Se en classi ie algo i hms ha e been applied in his esea ch
such as a i icial neu al ne wo k, C5.0, Chi-squa e Au oma ic in e ac ion de ec o , logis ic eg ession, linea
suppo ec o machine wi h penal y L1 & wi h penal y L2 and andom ee. The impo an ea u e selec ion
echnique was also applied o he da ase . Fo each classi ie , he esul s ha e been compu ed based on
(i) ull ea u es, (ii) co ela ion-based ea u e selec ion, (iii) W appe me hod ea u e selec ion, (i ) Leas
absolu e sh inkage and selec ion ope a o eg ession, ( ) syn he ic mino i y o e -sampling echnique wi h
leas absolu e sh inkage and selec ion ope a o eg ession selec ed ea u es, ( i) syn he ic mino i y o e -
sampling echnique wi h ull ea u es. F om he esul s, i is ma ked ha LSVM wi h penal y L2 is gi ing
he highes accu acy o 98.86% in syn he ic mino i y o e -sampling echnique wi h ull ea u es. Along
wi h accu acy, p ecision, ecall, F-measu e, a ea unde he cu e and GINI coe icien ha e been compu ed
and compa ed esul s o a ious algo i hms ha e been shown in he g aph. Leas absolu e sh inkage and
selec ion ope a o eg ession selec ed ea u es wi h syn he ic mino i y o e -sampling echnique ga e he bes
a e syn he ic mino i y o e -sampling echnique wi h ull ea u es. In he syn he ic mino i y o e -sampling
echnique wi h leas absolu e sh inkage and selec ion ope a o selec ed ea u es, again linea suppo ec o
machine ga e he highes accu acy o 98.46%. Along wi h machine lea ning models one deep neu al ne wo k
has been applied on he same da ase and i has been no ed ha deep neu al ne wo k achie ed he highes
accu acy o 99.6%.
INDEX TERMS Ch onic kidney disease, machine lea ning, p edic ion.
I. INTRODUCTION
Ch onic kidney Disease (CKD) means you kidneys a e dam-
aged and no il e ing you blood he way i should. The
p ima y ole o kidneys is o il e ex a wa e and was e om
The associa e edi o coo dina ing he e iew o his manusc ip and
app o ing i o publica ion was Ha una Chi oma .
you blood o p oduce u ine and i he pe son has su e ed
om CKD, i means ha was es a e collec ed in he body.
This disease is ch onic because o he damage g adually o e
a long pe iod. I is la e ing a common disease wo ldwide
[1]. Due o CKD may ha e some heal h oubles. The e a e
many causes o CKD like diabe es, high blood p essu e,
hea disease. Along wi h hese c i ical diseases, CKD also
17312 This wo k is licensed unde a C ea i e Commons A ibu ion 4.0 License. Fo mo e in o ma ion, see h ps://c ea i ecommons.o g/licenses/by/4.0/ VOLUME 9, 2021
P. Chi o a e al.: P edic ion o Ch onic Kidney Disease
depends on age and gende [2]. I you kidney is no wo king,
hen you may no ice one o mo e symp oms like abdominal
pain, back pain, dia hea, e e , nosebleeds, ash, omi ing.
The e a e wo main diseases o CKD: (i) diabe es and (ii) high
blood p essu e [3]. So ha con olling o hese wo diseases is
he p e en ion o CKD. Usually, CKD does no gi e any sign
ill kidney is damaged badly. CKD is being inc eased apidly
as pe he s udies hospi aliza ion cases inc ease 6.23 pe cen
pe yea bu he global mo ali y a e emains ixed [4]. The e
a e ew diagnos ic es s o check he condi ion o CKD:
(i) es ima ed glome ula il a ion a e(eGFR) (ii) u ine es
(iii) blood p essu e.
A. EGFR
eGFR alue shows ha how you kidney cleaning he blood.
I you eGFR alue is g ea e han 90, ha means he kidney
is no mal. I eGFR alue is less han 60, ha means you ha e
CKD [5].
B. URINE TEST
The doc o also asks o u ine es o kidney unc ionali y
because kidneys make u ine. I he u ine con ains blood and
p o ein [6], ha means you kidney is no wo king p ope ly.
C. BLOOD PRESSURE
Doc o measu es blood p essu e as Blood p essu e ange
shows how you hea is pumping blood. I eGFR alue
eaches less han 15, ha means he pa ien has end-s age kid-
ney disease. A his poin , he e a e only a ailable ea men s:
(i) dialysis and (ii) kidney ansplan . Pa ien ’s li e a e
dialysis depends on such ac o s as age, gende , equency
and du a ion o dialysis, physical mo emen o he body and
men al heal h [7]. I dialysis is no possible, he doc o has
only one solu ion, i.e., kidney ansplan a ion. Howe e , i is
ex emely expensi e [8].
The e o e, i is c i ical no ewo hiness in ea ly ecogni ion,
moni o ing and handling o he disease. I is essen ial o
p edic he s iding o CKD wi h app op ia e accu acy due
o i s dynamic and sec e i e na u e in he ea ly s ages and
pa ien abno mali y. Medical ea men o CKD is p esc ibed
by he s age. Any hing o he han his, i is e y impe a i e o
cha ac e ize he o ganiza ion o he in ec ion because i gi es
a ew indica ions. I unde pins he assu ance o undamen al
in e cessions and medica ions.
Medical ea men is a e y signi ican applica ion a ea o
in ellec ual in elligen sys ems [10]. A e wa ds, Da a mining
can play a big ole o ind ou hidden in o ma ion om he
huge pa ien medical and ea men da ase ha doc o s e-
quen ly ob ain om pa ien s o ge pieces o knowledge abou
he symp oma ic da a and o execu e p ecise ea men plans.
Da a mining can be ca ego ized as he me hod o ex ac ing
hidden in o ma ion om a huge da ase . Da a mining s a e-
gies a e connec ed and u ilized b oadly in a ious con ex s
and a eas. Using da a mining me hods, we may p edic , clas-
si y, il e and clus e da a. The objec i e s a es he algo i hm
p ocessing o a aining se con aining a se o a ibu es and
a ge s. Da a mining is sui able o mining in da a i he da ase
is huge bu we can also do i wi h he help o machine lea ning
wi h a small da ase . The machine lea ning can also ind
da a analysis and pa e n de ec ion [9]. A a ie y o heal h
da ase is p esen so machine lea ning algo i hms a e bes
i o imp o e he accu acy o diagnosis p edic ion [11]. As
heal hca e elec onic da ase g ows apidly, machine lea ning
algo i hms a e becoming mo e common in heal hca e. [12].
Qin e al. [13] p oposed da a asse ion and sample diagno-
sis achie able in CKD diagnosis. KNN is used o da a asse -
ion. Six classi ie s algo i hms used o accu acy o diagnosis:
logis ic eg ession, andom o es , suppo ec o machine,
K-nea es neighbo , nai e Bayes classi ie and eed- o wa d
neu al ne wo k. In hese classi ie s andom o es gi es be e
accu acy, i.e., 99.75%.
Vasquez-Mo ales e al. [14] de eloped a neu al ne wo k
model o isk p edic ion o Ch onic Kidney Disease de el-
opmen on 40000 ins ances da ase and hei model accu acy
was 95%.
Chen e al. [15] applied h ee models on he da ase ha
is p o ided by UCI. They used KNN, SVM and so inde-
penden modelling o class analogy (SIMCA) o inding he
isk calcula ion o pa ien using hese classi ie s. In which he
SVM and KNN model a ained, he bes accu acy o 99.7%
and SVM model has he g ea es capabili y o endu e noise
dis u bance.
Because CKD is in asi e, cos ly so ha many pa ien s
eached a las s ages wi hou ea men s. So ha ea ly de ec-
ion o his disease emains impo an . Besides, Ami galiye
[16] ga e he expe imen al esul o SVM machine lea ning
classi ie algo i hm wi h accu acy 93%.
Padmanaban and Pa hiban [17] sugges ed ha he ea ly
de ec ion o CKD o diabe ic pa ien s wi h he help o
machine lea ning classi ie s algo i hms. They collec ed da a
om Chennai based diabe es esea ch cen e and applied
Nai e Bayes and Decision ee on he da ase . Fo inding
he accu acy hey used Weka ool and concluded ha Naï e
Bayes classi ie achie ed he highes accu acy o 91%.
de Almeida e al. [18] in hei wo k applied Decision ee,
Random Fo es , Suppo Vec o Machine (SVM) and also
used SVM wi h linea , polynomial, sigmoid and RBF unc-
ions. Fo hei esea ch, hey used he MIMIC-II da abase.
They concluded ha andom o es and Decision ee go he
bes esul in he o m o p edic ion accu acy o 80% and 87%
espec i ely.
Guna a hne e al. [19] buil a model o a ious machine
lea ning classi ie s algo i hm and analysis o which algo i hm
is bes sui ed o he da ase . They used da ase p o ided by
UCI con aining 400 ins ances and 14 a ibu es. They con-
cluded ha he Mul iclass decision o es algo i hm was bes
i ed o he CKD da ase wi h an accu acy o 99.1%.
Pola e al. [20] used SVM algo i hm o CKD p edic ion.
Fo he accu a e esul , hey wo ked on an impo an ea u e.
Fo selec ing he co ec ea u e, hey used wo-app oach
W appe and il e wi h he SVM algo i hm. In he W appe ,
he e we e he g eedy s epwise sea ch engine o classi ie
VOLUME 9, 2021 17313
P. Chi o a e al.: P edic ion o Ch onic Kidney Disease
subse e alua o and bes i s sea ch engine o W appe
subse e alua o . In il e , he e we e he g eedy s epwise
sea ch engine o co ela ion ea u e sec ion subse ea u e
and bes i s sea ch engine o il e ed subse e alua o . The
esul s o all echniques we e compa ed and i was ound ha
SVM ga e he highes accu acy wi h il e ed subse e alua o ,
i.e. 98.5%.
Suja a D all, Gu deep Singh D all, Sugandha Singh,
Bha a D all e al. [21] wo ked on CKD da ase gi en by
UCI wi h 400 ins ances and 25 a ibu es. Fi s ly, da a was
p ep ocessed, he missing da a was ound, illed wi h 0, hen
ans o med and applied on he da ase . A e p ep ocess-
ing, au ho s applied algo i hm o impo an a ibu es and
ound 5 mos impo an ea u es and hen he classi ica ion
algo i hm: Naï e Bayes and K-Nea es Neighbo . The go en
esul KNN achie ed he highes accu acy.
Almasoud and Wa d [22] wo ked wi h CKD da ase o 400
ins ances and 25 a ibu es. They applied he il e ea u e
selec ion me hod on a ibu es and ound ha haemoglobin,
albumin and speci ic g a i y a e ea u e a ibu es in CKD
da ase . A e ea u e selec ion, hey ained he da ase and
alida ed wi h 10- old c oss- alida ion. The g adien boos -
ing algo i hm achie ed he highes accu acy o 99.1%.
Shanka e al. [23] applied h ee s eps on he same UCI
da ase : (i) da a p ep ocessing & ea u e selec ion (ii), algo-
i hms’ accu acy de e mina ion and (iii) die plan sugges ion.
In he ea u e selec ion me hod, hey applied wo app oaches:
one is he W appe and he o he is he LASSO me hod. A e
he ea u e selec ion me hod, 4 classi ica ion algo i hms we e
applied: Logis ic Reg ession, Random o es ee K-Nea es
Neighbo s, Neu al Ne wo k and Wide and Deep Lea ning.
Fo die plan sugges ion blood po assium le el was used. The
blood po assium le el was di ided in o h ee g oups based on
i s alue: Sa e Zone, Cau ion Zone and Dange zone.
Vijaya ani and Dhayanand [24] collec ed kidney unc ion
es (KFT) da ase om medical labs, esea ch cen es and
hospi als. The da ase con ained 584 ins ances and 6 a ibu es
and wo classi ie applied algo i hms: suppo ec o machine
(SVM) and a i icial neu al ne wo k (ANN). I was ound ha
ANN achie ed he highes accu acy o 87.7%.
Xiao e al. [25] used he da a o 551 pa ien s and applied
9 machine lea ning algo i hm: XGBoos , logis ic eg ession,
lasso eg ession, suppo ec o machine, andom o es ,
idge eg ession, neu al ne wo k, Elas ic Ne and K-nea es
neighbo . They e alua ed accu acy, ROC cu e, p ecision and
ecall and ound ha linea model ga e he highes accu acy.
Reshma e al. [31] used he ea u e selec ion echnique
on CKD Da ase . Fo ea u e selec ion, ACO me hod was
applied. ACO is he me a heu is ic algo i hm o he ea u e
selec ion. I is he ype o W appe me hod. In hei da ase ,
o al 24 a ibu es we e a ailable. A e applying ea u e
selec ion algo i hm, 12 ea u es was used o making he
model. The Suppo Vec o machine classi ie s algo i hm was
used o building he model.
Deepika e al. [32] buil a p ojec on p edic ion o Ch onic
Kidney Disease based on old da ase o CKD. The da ase had
24 a ibu es and 1 a ge a iable. Fo building he model,
hey applied KNN and Naï e Bayes supe ised machine
lea ning algo i hm. KNN achie ed highes accu acy 97 % and
Naï e Bayes achie ed 91% accu acy.
Ma e al. [33] p oposed he deep lea ning algo i hm o
p edic ing he Ch onic Kidney Disease s a ea ly s age. The
deep neu al ne wo k was buil om He e ogeneous Modi ied
a i icial neu al ne wo k algo i hm. Fo building he model,
ul asound images we e used. Fo compa ing he esul , he e
we e h ee di e en classi ie s: Suppo Vec o machine, a i-
icial neu al ne wo k and mul ilaye pe cep on.
UI Haq e al. [34] p oposed he machine lea ning model
o p edic he diabe es disease a ea ly s age. They concluded
ha machine lea ning can play i al ole in he heal hca e.
Amin e al. [35] p oposed machine lea ning model o he
p edic ion o Pa kinson’s disease a ea ly s age. Fo building
he model, hey used SVM classi ie . Fea u e selec ion algo-
i hms we e also applied o ex ac he impo an ea u es:
Relie and ACO ea u e selec ion algo i hm.
This esea ch a icle p ima ily aims o p edic whe he a
pe son has Ch onic Kidney Disease o no . In his pe cep ion,
se en di e en machine lea ning classi ie s we e applied on
he da ase . All he algo i hms we e unning wi h bo h ull
ea u es and selec ed ea u es. SMOTE was used o o e sam-
pling and all he esul s we e eco ded. All he machine lea n-
ing model esul s we e also compa ed wi h one deep neu al
ne wo k algo i hm. Deep lea ning neu al ne wo k was used
wi h wo hidden laye s. IBM SPSS Modele was applied o
compu a ional pu pose. The con ibu ion e eals he accu acy
es ima e o 99.6% when applying deep neu al ne wo k on he
da ase .
II. RESEARCH GAP
Un il now, in majo i y o cases ull ea u es ha e been aken
in o conside a ion. In his esea ch, ea u e op imiza ion was
ca ied ou , whe ein h ee di e en ea u e selec ion algo-
i hms we e applied o ind he algo i hm mos bene icial o
ex ac he impo an ea u e o he p edic ion o Ch onic
Kidney Disease. As many da ase s ha e imbalanced class,
class balancing is needed o inc easing he pe o mance o
classi ie model. In his esea ch SMOTE was used as a
class balance . The highes accu acy o 99.6% was achie ed
whe eas he a icle [22] p o ides an accu acy o 99.1% on
he same da ase . Acco ding o [15] he highes accu acy o
he model was 99.7%, bu hey wo ked on isk calcula ion o
he pa ien whe eas he main aim o he a icle is o p edic
Ch onic Kidney Disease.
III. METHODS AND MATERIALS
In his sec ion, he esea ch me hodology and a da ase will
be discussed.
A. DATASET
Ch onic Kidney Disease da ase is used o his esea ch
wo k. Many esea che s had also used his da ase [26].
This da ase is being p o ided by he UC I ine Machine
17314 VOLUME 9, 2021
P. Chi o a e al.: P edic ion o Ch onic Kidney Disease
Lea ning Reposi o y and i is a ailable on he UCI websi e.
This da ase con ains 400 ins ances and 24 a ibu es wi h
1 a ge a ibu e. The a ge a ibu e has labelled in wo-class
o ep esen CKD o non-CKD. The da ase was collec ed
om a ious hospi als in 2015. I con ains also missing alue.
The desc ip ion o all 24 a ibu es is ep esen ed in he able
1 below.
B. METHODOLOGY
In his esea ch, we ha e de eloped a model o p edic CKD
disease in pa ien s. The pe o mance o he model was es ed
on bo h all a ibu es and selec ed ea u es. Among ea u e
selec ion me hods he e we e W appe , Fil e and Embedded
[27] allowing o selec i al ea u es. Classi ie algo i hms
pe o mance was es ed on he selec ed ea u es. IBM SPSS
ool is used o p epa ing he model. The machine lea ning
classi ie s such as a i icial neu al ne wo k (ANN), C5.0,
logis ic eg ession, linea suppo ec o machine (LSVM),
K- nea es neighbo s (KNN) and andom ee we e used
o aining he model. Each classi ie alida ion and pe o -
mance ma ix we e compu ed. The p ocedu e o his esea ch
including i e s ages: (i) da ase p ep ocessing, (ii) ea u e
selec ion, (iii) classi ie applica ion, (i ) SMOTE and ( ) ana-
lyzing he pe o mance o he classi ie . Along wi h machine
lea ning models, a deep neu al ne wo k was applied o com-
pa ing he esul o machine lea ning models and deep neu al
ne wo k. A i icial Neu al ne wo k classi ie was used o his
pu pose. In his esea ch he signi icance o wo model we e
checked by s a is ic es ing namely McNema ’s es .
C. PREPROCESSING OF DATA
Da a p ep ocessing could be a s a egy ha is u ilized o
change o e he aw in o ma ion in o a clean da ase . I is a
he basic s ep o ain e e y machine lea ning classi ie
algo i hm. This echnique concludes such ac ions as handle
missing alues, escaling o he da ase , ans o m in o bina y
da a and s anda dize o he da ase . When he da ase included
a ibu es wi h a ying scales, escaling is used o scale he
da ase . The bina y ans o ma ion has been applied o con-
e he alue in o 0 and 1. All alues o e e y a ibu e a e
conside ed as 1 o abo e he h eshold and as 0 o below he
h eshold. S anda dized me hod ensu es ha each a ibu e
has mean 0 and s anda d de ia ion 1.
D. FEATURE SELECTION
Fea u e selec ion is needed o ained each machine lea ning
classi ie because wi hou emo ing unnecessa y a ibu es
om he da ase esul may be a ec ed. The classi ie algo-
i hm wi h ea u e selec ion gi es be e pe o mance and
educe he execu ion ime o he model. Fo his p ocess, h ee
di e en ea u e selec ion me hods we e used in his esea ch.
1) FILTER METHOD
The il e is one o he me hods o selec he app op ia e
ea u e. I selec s he ea u e on hei in eg al ea u es wi hou
in eg a ing any lea ning classi ie algo i hm. This me hod
gi es esul as e as compa ed o he w appe me hod. The
me hod assigns he sco e o e e y a ibu e based on hei
s a is ical co ela o be ween a ibu es. The e a e many il e
me hods a e a ailable, bu Co ela ion-based Fea u e Selec-
ion (CFS) me hod has been used. CFS is he algo i hm o
selec he ea u e-based on he a ibu e anks. I assigns he
ank o a ibu e subse as based on he co ela ion heu is ic
e alua ion unc ion [28]. The unc ion wo ks on he s a egy
ha c ea es wo class labels, one is co ela ed o class and
low co ela ed class and selec s only co ela ed label class
a ibu es.
2) WRAPPER METHOD
W appe me hod selec s he subse o ea u es based on a
p ecise machine lea ning algo i hm [29]. I used he g eedy
sea ch me hod o inding a possible subse o ea u es. The
me hod can be implemen ed wi h using any o he ollow-
ing algo i hms o wa d selec ion, backwa d elimina ion and
ecu si e elimina ion. In he esea ch, we used he o wa d
ea u e selec ion me hod. The o wa d ea u e selec ion i e -
a i ely selec s he ea u e. This p ocedu e s a s wi h he null
model and wo k i e a i ely and add he a ibu e in each s ep.
The a ibu e is keeping add in he model un il he a ibu e
does no imp o e model pe o mance.
3) EMBEDDED METHOD
The embedded me hod is decision ee algo i hm o ea u e
selec ion. I selec s he ea u e in each s ep wo ks ecu si ely
while he ee is g owing and spli he sample se in o a
smalle subse . The mos common decision ee algo i hm
a e: ID3, C4.5 and CART. The e a e o he a ailable me hod
s c ea ing linea models. The mos common me hods a e
LASSO [30] wi h L1 penal y and Ridge wi h L2 penal y.
In his esea ch LASSO (leas absolu e sh inkage and selec-
ion ope a o ) algo i hm has been used. I pe o ms wo main
asks: egula iza ion and ea u e selec ion. In egula iza ion, i
sh inks some ea u e coe icien s o ze o ha means ea u es
a e no impo an o he p edic o model.
E. CLASSIFICATION ALGORITHMS
Classi ica ion echnique is an impo an ea u e o supe ised
lea ning. Classi ie s lea n om he aining da ase and apply
on he es ing da ase o inding he a ge a ibu e. Below
he e a e classi ica ion echniques used in esea ch.
1) ARTIFICIAL NEURAL NETWORK
A i icial neu al ne wo k is a pa o a i icial in elligence.
I is a ype o supe ised machine lea ning. I s s uc u e is
he same as he human b ain. ANN also ha e neu ons and
jus like in human all neu ons a e in e connec ed o one
ano he , ANN neu ons a e connec ed o each o he in laye s
o he ne wo k. Neu ons he e a e known as nodes. ANN
can sol e he p oblem ha has been impossible o human
o s a is ical s anda ds. ANN consis s o h ee laye s: inpu ,
hidden and ou pu laye s. The inpu laye akes inpu and
weigh and passes o hidden laye o pe o ming calcula ion
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TABLE 1. Desc ip ion o A ibu es in he Da ase .
and inding he hidden s uc u es and pa e ns. The numbe o
hidden laye s can be inc eased as equi ed. The ou pu laye
compu es he ou pu . The weigh alues om he ou pu , i.e.
p edic ed, and ac ual alue we e ecompu ed and he ne wo k
again es a s o inding he class om he p e ious lea ning.
The e o e, ANN wo ks based on backp opaga ion.
2) C5.0
C5.0 is a ype o decision ee because i c ea es he decision
ee om he inpu . The ee has he numbe o b anches. I
u ilizes he ee s uc u e o model he ela ionship be ween
ea u es and po en ial ou comes. A each node o he ee,
he a ibu e o he da ase is chosen. I can handle nominal
and nume ic ea u es bo h. C5.0 is he ex ended e sion o he
C4.5 classi ica ion algo i hm and uses in o ma ion en opy
concep . En opy is used o inding he impu i y o ea u es.
In o ma ion en opy is p oduced based on he calcula ion o
pa en and child en opy alues. This p ocess is i e a i e and
wo ks un il he e is no he u he spli .
3) LOGISTIC REGRESSION
Logis ic eg ession is also a ype o supe ised lea ning algo-
i hm. I is a s a is ical model. The p obabili y o a ge alue
is p edic ed om logis ic eg ession. I is di ided he a ge
a ibu e in o wo-classes: success o no success. Fo success,
i e u ns 1 whe eas i e u ns 0 o no succeeding. Logis ic
eg ession is ep esen ed by equa ion 1:
P=1/(1+e^(−(b0+b1x+b2x^2)) (1)
whe e P is he p edic ed alue, b0, b1, b2a e biases and
x is is an a ibu e. I is used in a ious ield o machine
lea ning applica ion in social sciences and medical a ena,
o example, o spam de ec ion, diabe es de ec ion, cance
de ec ion, e c. Logis ic eg ession is he ad anced e sion o
linea eg ession. Th ough his echnique, we only conce n
abou he p obabili y o he ou come a iable.
4) CHAID
Chi-squa e au oma ic in e ac ion de ec ion (CHAID) is a ype
o decision ee echnique. I is used o de e mine he ela-
ionship be ween a iables. Nominal, o dinal and con inuous
da a can be used in CHAID o inding he ou come. Fo
each ca ego ical p edic o , all possible c oss- abula ion is
c ea ed in he CHAID model and i p ocess wo ks un il he
bes ou come is a ained. The a ge o dependen a iable
becomes a oo node in he ee, he a ge a iable is spli in o
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wo o mo e pa s as pe he ca ego ies in a ge a iable and
child o he oo node a e c ea ed using he s a is ical me hod
and a iable ela ionship. Such a p ocess will be ill lea nodes
o he ee. F es is used o he con inuous dependen a iable
and he Chi-squa e es is used o he ca ego ical dependen
a iable.
5) LINEAR SUPPORT VECTOR MACHINE (LSVM)
linea suppo ec o machine (LSVM) is he mode n pa ic-
ula ly as machine lea ning algo i hm o sol ing mul iclass
classi ica ion p oblem o he la ge da ase based on a simple
i e a i e app oach. I is c ea ed he SVM model in linea
CPU ime o he da ase . LSVM can be used o he high
dimensional da ase is he spa se and dense o ma . I is used
o sol ing he la ge da ase machine lea ning p oblems in
less expensi e compu ing esou ce. Suppo Vec o Machine
is a supe ised classi ie algo i hm. I is used ke nel ick o
sol ing he classi ica ion p oblem. Based on hese ans o -
ma ions, ideal edge is ound be ween he possible ou pu s.
SVM is used o he nonlinea ke nel, such as RBF. Fo
he linea ke nel, LSVM is an app op ia e choice. LSVM
classi ie is su icien o all linea p oblems.
6) K- NEAREST NEIGHBORS (KNN)
KNN is a simple ype o supe ised algo i hm. I can be used
o bo h classi ica ion and eg ession p oblems. Howe e , i is
la gely used o classi ica ion p oblems. KNN does no use
a pa icula aining s age and use all he da a o aining
so ha i is a lazy lea ning algo i hm and also i does no
conside any hing abou he unde lying da a, so ha is a non-
pa ame ic lea ning algo i hm. KNN s o es he whole da ase
because i has no model so ha he e is no lea ning equi ed.
When he new da a en e o p edic ing he ou comes, i com-
pa es K – neighbo s so ha selec ion o K’s alue is e y
impo an . The dis ance is calcula ed be ween wo al eady
label da a. The dis ance helps o ind he nea es neighbo
o he new da a. A Euclidian me hod is used o inding he
dis ance.
7) RANDOM TREE
The andom ee is a ype o supe ised classi ie s. I p oduces
lo s o dis inc lea ne s. The s ochas ic p ocess is used o
o m he ee. I is a ype o ensemble lea ning echnique o
classi ica ion. I wo ks he same as decision ee, bu a andom
subse o a ibu es uses o each spli . This algo i hm uses
o bo h classi ica ion p oblems and eg ession p oblems.
A g oup o andom ees is known as a o es . The andom
ees classi ie akes he inpu ea u e se and classi ies inpu
o e e y ee in he o es . The ou pu o he andom ee
selec s om he majo i y o o es. In he ee, e e y lea node
holds a linea model. The bagging aining algo i hm is used
o ain he model.
F. VALIDATION METHOD OF CLASSIFIERS
The da ase was di ided in o pa s: aining da ase and es ing
da ase . IBM SPSS modelle was used o he pa i ion and
TABLE 2. Con usion Ma ix.
p edic ion o he esul . The aining da ase con ains 50% o
he da a and emaining da a is conside ed as he es ing da a.
The ype ool o IBM SPSS was applied o changing he ype
o a ibu es. The pe o mance e alua ion ma ix was ecei ed
o each classi ica ion algo i hm.
G. PERFORMANCE EVALUATION MEASURE
Va ious e alua ion ma ices we e used o checking he pe -
o mance o he classi ie . Fo his pu pose, he con usion
ma ix was used. I is a 2∗2 ma ix due o wo classes in he
da ase . The con usion ma ix gi es wo ypes o co ec p e-
dic ion o he classi ie and wo ypes o inco ec p edic ion
o he classi ie . The con usion ma ix is p esen ed in Table 2.
1) CONFUSION MATRIX DESCRIPTION
TP: T ue Posi i e means ou pu as posi i e such ha p edic ed
esul is co ec ly classi ied.
TN: T ue Nega i e means ou pu as nega i e such ha
p edic ed esul is co ec ly classi ied.
FP: False Posi i e means ou pu as posi i e such ha p e-
dic ed esul is inco ec ly classi ied.
FN: False Nega i e means ou pu as nega i e such ha
p edic ed esul is inco ec ly classi ied.
2) CLASSIFICATION ACCURACY
Classi ica ion accu acy shows he co ec a e o p edic ion
esul s. I compu es om he con usion ma ix. The classi i-
ca ion accu acy is ound by equa ion 2:
accu acy =TP +TN
TP +TN +FP +FN ∗100 (2)
3) CLASSIFICATION ERROR
Classi ica ion e o shows he inco ec a e o p edic ion
esul s. I compu es om he con usion ma ix. The classi-
ica ion e o is ound by equa ion 3:
E o =FP +FN
TP +TN +FP +FN∗100 (3)
4) PRECISION
P ecision is an impo an model pe o mance e alua ion
ma ix. I is he ac ion o ela ed ins ances among he o al
e ie ed ins ances. I is a posi i e p edic ed alue. The p e-
cision is calcula ed as ollows in equa ion 4:
P ecision =TP
TP +FP ∗100 (4)
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5) RECALL
Recall is also an impo an model pe o mance e alua ion
ma ix. I is he ac ion o ela ed ins ances among he o al
numbe o e ie ed ins ances. The ecall is calcula ed as
ollows in equa ion 5:
Recall =TP
TP +FN ∗100 (5)
6) F-MEASURE
I is also known as F Sco e. F-measu e is calcula ed so as
o measu e he accu acy o es . I is calcula ed om he
p ecision and ecall by equa ion 6:
F−Measu e =2∗P ecision ∗Recall
P ecision +Recall (6)
7) ROC AND AUC
The pe o mance o he classi ica ion model is measu ed
om he Recei e ope a ing a cha ac e is ic cu e (ROC).
ROC is a g aph ha is c ea ed o ue posi i e a e s. alse
posi i e a e a di e en classi ica ions h eshold. The en i e
a ea unde he ROC cu e is known as a ea o he cu e
(AOC). I gi es a collec i e measu e o pe o mance ac oss
all achie able classi ica ion’s h eshold.
8) GINI COEFFICIENT
I is also known as GINI index. I is a measu e o s a is ical
dis ibu ion. I is used o measu e he inequali y amongs
alues o a ibu es. I is also possible o say ha i calcu-
la es he impu i y o a pa icula a ibu e in he o m o
deg ee o p obabili y.
H. SMOTE
Syn he ic Mino i y O e sampling Technique (SMOTE) is
used o o e sampling he mino i y class. I is also known
as a balance . I akes he whole da ase as inpu bu wo ks
only on mino i y class. I inc eases he pe cen age o mino i y
class. SMOTE used KNN o inding new ins ances. I does
no make any change in he majo i y cases. The new examples
a e no simply duplica ing o exis ing mino i y cases. Ins ead,
he calcula ion akes es s o he componen space o each
a ge class and i s closes neighbo s and hen p oduces new
models ha join a ibu es o he objec i e case wi h he
highligh s o i s neighbo s. This me hodology builds he high-
ligh s accessible o each class and makes es s p og essi ely
b oad.
The ma hema ical symbols used in his esea ch is shown
in he able 3.
I. STATISTICS TEST FOR MODEL COMPARISON
Fo he pu pose o compa ing wo models, McNema ’s es
was applied on he p edic ed ou pu o wo models. The
McNema ’s es is used o de e mine whe he he e a e di -
e ences on bipola dependen a iables be ween wo ela ed
g oups. In his es 2∗2 con ingency ma ix is o med o wo
g oups and p alue is calcula ed. Fo his pu pose, signi i-
cance le el α=0.05 is conside ed. I p < α, we can ejec he
null hypo hesis. I p > α, we ail o ejec he null hypo hesis.
I p alue is less han α, i means bo h models show a
signi ican di e ence as ega ds he hypo hesis. Howe e , i p
> α, he di e ence would no be ega ded as s a is ically
signi ican .
IV. EXPERIMENTAL AND RESULT
The esul o his esea ch including all ou comes and clas-
si ica ion models om di e en pe cep ion will be discussed
in his sec ion. IBM SPSS model is shown in igu e 1. Fi s ,
he pe o mance o di e en machine lea ning algo i hm
iz. an a i icial neu al ne wo k, logis ic eg ession, C5.0,
CHAID, andom ee, K-nea es neighbo s and linea suppo
ec o machine ha e been checked on all ea u es. Second,
pa ea u e selec ion algo i hm CFS, o wa d W appe and
LASSO ha e been applied on he da ase o ind he impo -
an ea u es. Thi d, he pe o mance o all abo e-men ioned
classi ica ion algo i hm on impo an ea u es was checked.
Fou h, SMOTE il e was also applied o he da ase and he
esul o classi ie s we e checked. Va ious ools we e used.
Weka ool was used o CFS and Fo wa d me hod. s udio
was used o LASSO. IBM SPSS Modele was used o he
pe o mance o classi ie s. Deep neu al ne wo k was buil in
IBM SPSS modele . ANN was used wi h 2 hidden laye s o
building deep neu al ne wo k. Twel e nodes we e used in
hidden laye 1 and eigh nodes we e in hidden laye 2.
A. RESULT WITHOUT FEATURES SELECTION
In his subsec ion, he ull ea u es o he da ase we e used
and he esul was es ed on all se en machine lea ning
classi ica ion algo i hms wi h 50% o aining da a and 50%
o es ing da a. The compa ison ma ix was c ea ed o all
algo i hms. Wi h he esul an ma ix, h ee g aphs we e also
c ea ed o checking he a ia ion o a ious classi ie s. The
i s g aph p o ides a compa ison o all classi ie ’s accu acy,
p ecision and ecall. The second one con ains he a ia ion o
AUC and he hi d one includes he a ia ion o F-measu e.
The compa ison o all classi ie s showed ha he C5.0 algo-
i hm achie ed he highes accu acy, i.e. 96.10%. The alue
o all pa ame e s o C5.0: p ecision was 92.40%, ecall was
97.30%, F-measu e was 94.80%, AUC was 97.80% and GINI
index was 0.96. The a i icial neu al ne wo k was ained on
3 hidden laye s. The ANN achie ed accu acy o 94.63%,
p ecision o 93.24% and ecall o 92%. The logis ic eg es-
sion achie ed accu acy o 71.71%, p ecision o 56.48% and
ecall o 98.6%. The CHAID algo i hm achie ed accu acy
o 96%, p ecision o 93.50% and ecall o 92%. The LSVM
wi h Penal y L1 and Lambda 0.5 achie ed accu acy o 92.2%,
p ecision o 83.90% and ecall o 97.33%. The LSVM wi h
Penal y L2 and Lambda 0.5 achie ed accu acy o 94.63%,
p ecision o 90% and ecall o 96%. The KNN ga e he wo s
esul o his da ase wi h a K alue o 5: accu acy o 64.39%,
p ecision o 59.01% and ecall o 96%. The andom ee algo-
i hm achie ed accu acy o 90.73%, p ecision o 83.34% and
ecall o 93%. As a esul , ANN achie ed he highes AUC
and C5.0 achie ed F-measu e. The esul o all classi ie s is
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TABLE 3. Desc ip ion o Used Ma hema ical Symbol.
as ollows in able 4. The compa ison o p ecision, ecall and
accu acy is desc ibed in igu e 2. The compa ison o AUC
is desc ibed in igu e 3. The compa ison o F-Measu e is
desc ibed in igu e 4.
B. RESULT OF CORRELATION-BASED FEATURE SELECTION
(CFS)
In his subsec ion, he impo an ea u es we e selec ed by
CFS algo i hm o pass in he classi ie algo i hms o p edic -
ing he ou comes. Six mos impo an ea u es we e used o
inding he ou comes such as bp, pc, pe, ane, pc and bc.
As pe he CFS algo i hm, bp and pc a e he mos impo an
ac o s o p edic ing Ch onic Kidney Disease. The esul o
CFS algo i hm is shown in igu e 5. The pe o mance o all
se en classi ie s was desc ibed in able 5. The LSVM wi h
Penal y L2 and Lambda 0.5 achie ed he highes accu acy
o selec ed ea u es om he CFS algo i hm wi h 95.12%
accu acy, 93.34% p ecision and 93.34% ecall. C5.0 and
CHAID achie ed an accu acy o 92.68%. The C5.0 algo i hm
achie ed 85.71% p ecision and 96% ecall. The CHAID
algo i hm achie ed 92.68% accu acy, 96.87% p ecision and
83% ecall. The ANN algo i hm achie ed 91.71% accu acy,
89.19% p ecision and 88% ecall. The logis ic eg ession
algo i hm achie ed he lowes accu acy o 51.22%, 96.87%
p ecision and 92.54% ecall. The LSVM wi h Penal y L1 and
Lambda 0.5 achie ed 93.66% accu acy, 87.8% p ecision
and 96% ecall. The KNN achie ed o his da ase wi h
a K alue o 5 accu acy o 53.17%, p ecision o 97.05%
and ecall o 100%. The andom ee algo i hm achie ed
87.80% accu acy,82.05% p ecision and 85% ecall. As om
he esul , LSVM wi h penal y L1 achie ed he highes
AUC. The compa ison o p ecision, ecall and accu acy is
desc ibed in igu e 5. The compa ison o he GINI index is
shown in igu e 6. The compa ison o AUC is desc ibed in
igu e 7.
C. RESULT OF WRAPPER FORWARD FEATURE SELECTION
AND CLASSIFICATION
In his subsec ion, he impo an ea u es we e selec ed by
W appe o wa d ea u e selec ion algo i hm o pass in he
classi ie algo i hms o p edic ing he ou comes. Six mos
impo an ea u es we e used o inding ou comes such as
hemo, h n, dm, cad, pe, al. As pe he CFS algo i hm, hemo
and h n a e he mos impo an ac o s o p edic ing Ch onic
Kidney Disease. The esul o he W appe algo i hm is
shown in igu e 9. The esul o all classi ie algo i hm pe o -
mance is desc ibed in able 6. The C5.0 achie ed he highes
accu acy wi h he W appe algo i hm, namely 96.1% accu-
acy, 98.55% p ecision and 90.67% ecall. ANN, CHAID
and he andom ee also ga e a good esul . The ANN
algo i hm achie ed 94.63% accu acy, 90% p ecision and 96%
ecall. The CHAID algo i hm achie ed 94.63% accu acy,
93.24% p ecision and 92% ecall. The andom ee algo i hm
achie ed 94.63% accu acy, 93.24% p ecision and 92% ecall.
The logis ic eg ession algo i hm achie ed 78.54% accu acy,
98.55% p ecision and 100% ecall. The LSVM wi h Penal y
L1 and Lambda 0.5 achie ed 94.15% accu acy, 88.89% p eci-
sion and 96% ecall. The LSVM wi h Penal y L2 and Lambda
0.5 achie ed 93.66% accu acy, 87.80% p ecision and 96%
ecall. The KNN ga e he wo s esul o his da ase wi h
a K alue o 5 76: 10% accu acy, 95.58% p ecision and
95.58% ecall. As om he esul , LSVM achie ed he highes
AUC. The compa ison o p ecision, ecall and accu acy is
desc ibed in igu e 10. The compa ison o he GINI index
is shown in igu e 11. The compa ison o AUC is desc ibed
in igu e 12.
D. RESULT OF LASSO FEATURE SELECTION
In his subsec ion, he impo an ea u es we e selec ed by
LASSO ea u e selec ion algo i hm o passe in he classi ie
algo i hms o p edic ing he ou comes. Six mos impo an
ea u es we e used o inding ou comes such as bc, pc, al,
ba, su, pcc. As pe he LASSO FS algo i hm, bc and pc
a e he mos impo an ac o s o p edic ing Ch onic Kidney
Disease. The esul o he LASSO FS algo i hm is shown
in igu e 13. The esul o algo i hm pe o mance o all
se en classi ie s is desc ibed in able 7. LSVM and CHAID
achie ed he highes accu acy o 97.07%. LSVM wi h bo h
penal y L1 and L2 achie ed 97.07% accu acy, 98.59% p e-
cision and 93.33% ecall. The CHAID algo i hm achie ed
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FIGURE 1. IBM SPSS model o kidney disease p edic ion.
97.07% accu acy, 100% p ecision and 92% ecall. The ANN
algo i hm achie ed 94.63% accu acy, 90% p ecision and 96%
ecall. The andom ee algo i hm achie ed 90.24% accu acy,
80.90% p ecision and 96% ecall. The logis ic eg ession
algo i hm achie ed 74.15% accu acy, 80.23% p ecision and
100% ecall. The andom ee algo i hm achie ed 88.78%
accu acy, 78.26% p ecision and 96% ecall. The KNN ga e
he wo s esul o his da ase wi h a K alue o 5: 56.59%
accu acy, 92% p ecision and 100% ecall. As om he esul ,
LSVM achie ed he highes AUC. The compa ison o p e-
cision, ecall and accu acy is desc ibed in igu e 13. The
compa ison o he GINI index is shown in igu e 14. The
compa ison o AUC is desc ibed in igu e 15 shows.
E. RESULT OF SMOTE
As he abo e esul , i was obse ed ha he highes accu acy
achie ed on he selec ed ea u es was gi en by LASSO ea-
u e selec ion me hod. Thus, SMOTE echnique was applied
on ull ea u es and on selec ed ea u es gi en by LASSO
eg ession me hod. The pe o mance o ANN, CHAID,
LSVM and Random T ee was checked by SMOTE. These
classi ica ion algo i hms we e pe o med e y well in all
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FIGURE 15. Pe o mance o GINI index o all classi ie s a e LASSO ea u e selec ion.
FIGURE 16. Pe o mance o a ea unde he cu e o all classi ie s a e LASSO ea u e selec ion.
FIGURE 17. Compa ison o p ecision, ecall and accu acy o all classi ie s wi h SMOTE and selec ed ea u es.
p ecision and 91.38% ecall. LSVM wi h penal y L1 achie ed
96.53% accu acy, 91.04% p ecision and 100% ecall. ANN
achie ed 96.47% accu acy, 98.14% p ecision and 91.38%
ecall. C5.0 achie ed 96.45% accu acy, 96.61% p ecision and
93.44% ecall. The Random T ee achie ed 91.43% accu acy,
84.72% p ecision and 94% ecall. The compa ison g aph o
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FIGURE 18. Pe o mance o AUC o all classi ie s wi h SMOTE and selec ed ea u es.
FIGURE 19. Pe o mance o F-Measu e o all classi ie s a e LASSO ea u e selec ion and SMOTE.
TABLE 8. Pe o mance o Classi ie s Wi h SMOTE and Selec ed Fea u es.
p ecision, ecall and accu acy o all algo i hms is shown
in igu e 20. The compa ison g aph o AUC o all algo i hms
is shown in igu e 21. The compa ison g aph o F-Measu e o
all algo i hms is shown in igu e 22.
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FIGURE 20. Compa ison o p ecision, ecall and accu acy o all classi ie s wi h SMOTE and ull ea u es.
FIGURE 21. Pe o mance o AUC o all classi ie s wi h SMOTE and ull ea u es.
FIGURE 22. Pe o mance o F-Measu e o all classi ie s a e LASSO ea u e selec ion and SMOTE.
F. COMPARISION MATRIX OF ALL EXPERIMENTS
I was obse ed ha ANN, C5.0, CHAID, LSVNM and Ran-
dom ee pe o med well on he conside ed CKD da ase .
The Logis ic eg ession and KNN ha e no gi en ou comes
as expec ed. So, he compa ison able has been c ea ed o
i e bes -pe o med algo i hms o all di e en echnique ype.
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TABLE 9. Pe o mance o Classi ie s Wi h SMOTE and Full Fea u es.
FIGURE 23. Compa ison o all classi ie models.
TABLE 10. Classi ie Pe o mance in Va ious Models.
The esul is desc ibed in able 10. The accu acy compa ison
g aph is shown in igu e 23.
G. PERFORMANCE OF LSVM IN ALL TECHNIQUES
As he abo e esul , LSVM wi h penal y L2 ga e a be e
esul in all echniques is i was discussed p e iously. In his
sec ion, he pe o mance o LSVM will be discussed. The
able 11 shows he esul o LSVM in all echniques. Along
wi h he able, he e is he g aph on he able da a. Fig-
u e 24 shows he compa ison o LSVM in all echniques.
H. VALIDATE MACHINE LEARNING MODEL
To alida e he indings abo e, he s udy includes esul s
om ano he da a se ound a The Cance Imaging A chi e
17330 VOLUME 9, 2021
P. Chi o a e al.: P edic ion o Ch onic Kidney Disease
FIGURE 24. Compa ison o LSVM in di e en models.
TABLE 11. Pe o mance o LSVM in all Machine Lea ning Models.
(TCIA). The da ase has 210 ins ances o kidney disease
pa ien . I con ains 48 a ibu es and 1 a ge a iable. The
da ase was used on he same models applied ea lie . These
indings a e gi en below and a e, in gene al, compa able
o ea lie esul s wi h no signi ican di e ences obse ed.
Though, he ou come o applying machine lea ning models
la gely dependens on he speci ic da ase , he expe imen s
abo e alida e ea lie indings, namely, SMOTE wi h ull
ea u es esul . Table 12 shows he esul o bo h da ase s.
I. PERFORMANCE OF DEEP NEURAL NETWORK
In his esea ch wo k, a i icial neu al ne wo k was used o
machine lea ning and deep neu al ne wo k-based analysis.
Fo machine lea ning a i icial neu al ne wo k used only
single hidden laye , bu o he usage o he a i icial neu al
ne wo k as a deep neu al ne wo k mo e hidden laye s can
be added. So as o es he pe o mance o machine lea ning
classi ie algo i hms, one deep neu al ne wo k model was
buil and he esul s we e no ed. In some cases, he deep
neu al ne wo k ga e s ong esul and impo an ea u es we e
ex ac ed by i sel , ha is, no ea u e selec ion algo i hm was
equi ed. The same da ase was used o building a deep
neu al ne wo k. I was no ed ha a deep neu al ne wo k
achie ed he highes accu acy o 99.6% and i was be e han
o he machine lea ning models.
V. DISCUSSION OF MACHINE LEARNING MODELS
All he machine lea ning models bu Logis ic and KNN
classi ie s gi e sa is ac o y esul and ha e he negligible
di e ence be ween p ecision and ecall alues. In compa ison
wi h hem p ecision o Logis ic and KNN classi ie s is low
whe eas ecall is high. I indica es ha hese wo classi ie s
gi e many False posi i e esul s due o unbalanced da ase .
Logis ic and KNN algo i hms ha e no enough capaci y o
dis inguish be ween posi i e class and nega i e class as he
ela ed AUC sco e is e y low. Along wi h AUC, he GINI
coe icien is also no sa is ac o y. Hence, Logis ic and KNN
a e no sui able o he p edic ion o CKD. In all cases LSVM
wi h L1 and L2 penal y has he bes p ecision, ecall, AUC
sco e and GINI coe icien and he model achie ed he highes
accu acy in majo i y cases.
VI. PERFORMANCE COMPARISON OF MACHINE
LEARNING MODEL AND DEEP NEURAL NETWORK
All he machine lea ning model esul s was discussed in he
able 10 and acco ding o i LSVM wi h penal y L2 pe o med
bes in SMOTE wi h ull ea u es and achie ed he highes
accu acy o 98.46%. As discussed, he deep neu al ne wo k
achie ed he highes accu acy om among all models wi h
99.6%. In o de o compa e he pe o mance o wo models,
McNema ’s es was applied. Fo his es , he highes accu-
acy was achie ed by machine lea ning model, i.e., LSVM
VOLUME 9, 2021 17331
P. Chi o a e al.: P edic ion o Ch onic Kidney Disease
TABLE 12. Valida ion o Machine Lea ning Model.
wi h SMOTE o all ea u es and a deep neu al ne wo k was
aken and hei signi ican alue was no ed. The p alue o
his es was 0.29 and i is g ea e han signi ican le el (α=
0.05) and, hence, we would ejec hypo hesis.
VII. CONCLUSION
This a icle objec s o p edic Ch onic Kidney Disease based
on ull ea u es and impo an ea u es o CKD da ase .
Fo ea u e selec ion h ee di e en echniques ha e been
applied: co ela ion-based ea u e selec ion, W appe me hod
and LASSO eg ession. In his pe cep ion, se en classi ie s
algo i hm we e applied iz. a i icial neu al ne wo k, C5.0,
logis ic eg ession, CHAID, linea suppo ec o machine
(LSVM), K-Nea es neighbo s and andom ee. Fo each
classi ie , he esul s we e compu ed based on ull ea-
u es, selec ed ea u es by CFS, selec ed ea u es by W ap-
pe , selec ed ea u es by LASSO eg ession, SMOTE wi h
selec ed ea u es by LASSO, SMOTE wi h ull ea u es.
I was obse ed ha LSVM achie ed he highes accu acy
o 98.86% in SMOTE wi h ull ea u es. All classi ie s
algo i hms pe o med well on ea u es selec ed by LASSO
eg ession wi h SMOTE and wi hou SMOTE. SMOTE wi h
ull ea u es ga e he bes esul o all 5 classi ie s. In his
esea ch, a o al o 7 classi ie s we e used. Howe e , Logis ic
and KNN did no gi e sui able esul s and i was why hey
we e no used in SMOTE. As pe he esul , i is concluded
ha SMOTE is a bes echnique o balancing a da ase .
I is no ed ha SMOTE ga e be e esul s wi h selec ed ea-
u es by LASSO eg ession as compa e o wi hou SMOTE
on LASSO eg ession model. LSVM achie ed he highes
accu acy in all expe imen s as compa ed o o he classi ie s
algo i hms.
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PANKAJ CHITTORA ecei ed he B.Tech. deg ee
in in o ma ion echnology om he Na ional Ins i-
u e o Technology, Du gapu , in 2010, and he
M.Tech. deg ee in compu e science and enginee -
ing in 2012. He is cu en ly pu suing he Ph.D.
deg ee om he Depa men o Compu e Sci-
ence and Enginee ing, Manipal Uni e si y Jaipu ,
Jaipu . He has en yea s o expe ience as a Fac-
ul y wi h he Compu e Science and Enginee ing
Depa men . He is doing esea ch in he ield o
heal hca e. His cu en esea ch in e es s include da a science, machine
lea ning, da abase, p og amming, in o ma ion secu i y, and he analysis o
algo i hms. He has deli e ed a ious subjec including da a s uc u e, algo-
i hms analysis, heo y o compu a ion, compile cons uc ion, and o he s.
SANDEEP CHAURASIA (Senio Membe , IEEE)
is cu en ly a P o esso wi h he Depa men
o CSE, School o Compu ing and I.T., Mani-
pal Uni e si y Jaipu , Jaipu . He has mo e han
12 yea s o ich expe ience in academics and
one yea in indus y. He has mo e han mo e
han 30 publica ions in in e na ional/na ional jou -
nals/con e ence p oceedings. His esea ch in e -
es s include machine lea ning, so compu ing,
algo i hms, and a i icial in elligence. He is asso-
cia ed wi h machine lea ning o mo e han se en yea s. He is cu en ly
wo king in he a ea o applica ion o machine/deep lea ning in na u al
language p ocessing like seman ic analysis and lexical analysis. He is also
guiding ou Ph.D. s uden s in he a ea o NLP, in usion de ec ion, and
ood adul e a ion using AI echniques. He is also an ac i e membe o
special in e es g oup and ini ia i e by MIR labs o connec he esea che s
and p o essional ac oss he globe. He is a Senio Membe o LMCSI and
MACM, and a membe o Machine In elligence Resea ch Labs, USA. He
is also membe o e iewe boa d o a ious jou nals and echnical p og am
commi ee o se e al epu ed con e ences.
PRASUN CHAKRABARTI (Senio Membe ,
IEEE) ecei ed he Ph.D. (Engg.) deg ee om
Jada pu Uni e si y, in 2009. He is cu en ly an
Execu i e Dean (Resea ch and In e na ional Link-
age) and also an Ins i u e Dis inguished Senio
Chai P o esso wi h he Depa men o Compu e
Science and Enginee ing, Techno India NJR Ins i-
u e o Technology. He has se e al publica ions,
books and 31 iled Indian pa en s in his c edi . He
has supe ised en Ph.D. candida es success ully.
On a ious esea ch assignmen s, he has isi ed Waseda Uni e si y, Japan,
(2012 a ailing p es igious INSA-CICS a el g an ), Uni e si y o Mau i ius,
in 2015, Nanyang Technological Uni e si y Singapo e, in 2015, 2016, and
2019, Lincoln Uni e si y College Malaysia, in 2018, he Na ional Uni e si y
o Singapo e, in 2019, Asian Ins i u e o Technology, Bangkok, Thailand,
in 2019, and ISI Delhi, in 2019. He is a Fellow o IETE, ISRD, U.K., IAER,
London, AE(I), and CET(I).
GAURAV KUMAWAT ecei ed he M.Tech. deg ee
om Paci ic Uni e si y. He is cu en ly pu su-
ing he Ph.D. deg ee om he Depa men o
Compu e Science and Enginee ing, Manipal Uni-
e si y Jaipu , Jaipu . He has a o al expe ience
o 14 Yea s as a Facul y wi h he CSE Depa men .
His esea ch in e es s include C p og amming,
objec -o ien ed p og amming using C++, co e
ja a, ad ance ja a, unix p og amming, web p o-
g amming, ope a ing sys ems, compu e a chi ec-
u e, in o ma ion secu i y, p og amming analysis, and da a analysis.
TULIKA CHAKRABARTI ecei ed he Ph.D. (Sc.)
deg ee om he Indian Ins i u e o Chemical Biol-
ogy, Jada pu Uni e si y, in 2013. She is cu -
en ly an Assis an P o esso (Senio G ade) wi h
Si Padampa Singhania Uni e si y, Udaipu . She
has se e al publica ions, books and iled pa en s
o he c edi . She is a na ional me i schola -
ship holde in bo h 10 h and 12 h g ade. She has
isi ed NUS, NTU, Lincoln Uni e si y College,
Malaysia, and AIT, Bangkok, on se e al academic
assignmen s.
VOLUME 9, 2021 17333
P. Chi o a e al.: P edic ion o Ch onic Kidney Disease
ZBIGNIEW LEONOWICZ (Senio Membe ,
IEEE) ecei ed he M.S. and Ph.D. deg ees in elec-
ical enginee ing om he W oclaw Uni e si y
o Science and Technology, in 1997 and 2001,
espec i ely, and he Habili a ion deg ee om
he Bialys ok Uni e si y o Technology, in 2012.
Since 1997, he has been wi h he Elec ical Engi-
nee ing Facul y, W oclaw Uni e si y o Technol-
ogy. He also ecei ed he wo i les o a Full
P o esso om he P esiden o Poland, in 2019,
and he P esiden o he Czech Republic. Since 2019, he has been a P o esso
wi h he Depa men o Elec ical Enginee ing, whe e he is cu en ly he
Head o he Chai o elec ical enginee ing undamen als.
MICHAŁ JASIŃSKI (Membe , IEEE) ecei ed he
M.S. and Ph.D. deg ees in elec ical enginee -
ing om he W oclaw Uni e si y o Science and
Technology, in 2016 and 2019, espec i ely. Since
2018, he has been wi h he Elec ical Enginee -
ing Facul y, W oclaw Uni e si y o Technology,
whe e he is cu en ly an Assis an P o esso . He
has au ho ed o coau ho ed mo e han 60 scien i ic
publica ions. His esea ch in e es s include using
big da a in powe sys em especially in poin o
powe quali y.
ŁUKASZ JASIŃSKI ecei ed he deg ee in elec-
ical enginee ing om he W oclaw Uni e si y
o Technology, Poland, and he Ph.D. deg ee in
dyscypine o au oma ion, elec onics and elec ical
enginee ing om he W oclaw Uni e si y o Tech-
nology. In 2020, he s a ed wo king as a designe
o elec ical ins alla ions in he design o ice. His
esea ch in e es includes big da a in powe sys-
ems, especially in poin o powe quali y.
RADOMIR GONO (Senio Membe , IEEE)
ecei ed he M.Sc., Ph.D., Habili a e Ph.D., and
P o esso deg ees in elec ical powe enginee -
ing in 1995, 2000, 2008, and 2019, espec i ely.
Since 1999, he has been wi h he Depa men
o Elec ical Powe Enginee ing, VSB–Technical
Uni e si y o Os a a, Czech Republic, whe e he
is cu en ly a P o esso and he Vice Head o he
depa men . His cu en esea ch in e es s include
elec ic powe sys ems eliabili y, he op imiza ion
o main enance, and enewable ene gy sou ces.
ELŻBIETA JASIŃSKA ecei ed he Ph.D. deg ee
om Poznań Uni e si y Technology, in 2013.
Since 2019, she has been wi h he Facul y o
Law, Adminis a ion and Economics, Uni e si y
o W oclaw, whe e she is cu en ly an Assis an
P o esso . She has au ho ed o coau ho ed mo e
han 70 scien i ic publica ions. He esea ch in e -
es s include sus ainable de elopmen , co po a e
socially esponsible, enewable ene gy esou ces,
and i ual powe plan s.
VADIM BOLSHEV ecei ed he M.S. deg ee in
elec ical enginee ing om O el S a e Ag a ian
Uni e si y, in 2012, and he Ph.D. deg ee in elec-
ical enginee ing in 2020. He gained expe ience
in he indus y as an elec ician, an elec ical engi-
nee , a chie enginee om 2010 o 2018. Since
2018, he has been a Resea che wi h he Labo-
a o y o Powe and Hea Supply, Fede al Scien-
i ic Ag oenginee ing Cen e VIM. His scien i ic
ac i i y is o de elop me hods and ools aimed a
imp o ing powe supply e iciency including he de elopmen o me hods
and de ices o moni o ing powe quali y and he echnical s a e o powe
supply sys em elemen s.
17334 VOLUME 9, 2021