350
In e na ional Jou nal o Ad ance and Applied Resea ch
www.ijaa .co.in
ISSN – 2347-7075
Impac Fac o – 8.141
Pee Re iewed
Bi-Mon hly
Vol. 6 No. 38
Sep embe - Oc obe - 2025
Pe o mance E alua ion o Machine Lea ning Based Classi ie Techniques In
P os a e Cance P edic ion using No el Decision T ee and Nai e Bayes
Classi ica ion Techniques
Ms. Radhika Bhis e
Compu e Science Depa men ,
D . D Y Pa il A s, Comme ce and Science College Aku di, Pune 411044, INDIA
Co esponding Au ho – Ms. Radhika Bhis e
DOI - 10.5281/zenodo.17317444
Abs ac :
P os a e cance is he mos diagnosed malignancy wo ldwide and he six h leading cause o
cance - ela ed dea h in men. Diagnosis is p ima ily based on p os a e-speci ic an igen es ing,
magne ic esonance imaging scans, and p os a e issue biopsies, al hough p os a e-speci ic an igen
es ing o sc eening emains con o e sial. New diagnos ic echnologies a e now a ailable, including
isk s a i ica ion bioassay es s, ge mline es ing, and a ious posi on emission omog aphy scans.
When con ined o he p os a e, he disease is conside ed localized and po en ially cu able. I he
disease has sp ead ou side he p os a e, bisphosphona es, ank ligand inhibi o s, ho monal ea men ,
chemo he apy, adiopha maceu icals, immuno he apy, ocused adia ion, and o he a ge ed
he apies can be used. This ac i i y p o ides a comp ehensi e e iew o he cu en e alua ion and
managemen o p os a e cance , highligh ing he ole o he in e p o essional eam in imp o ing ca e
o a ec ed pa ien s. The p oposed pape discussed ML based classi ie echniques o p edic ion o
a ec ed p os a e cance glands. The Decision ee and naï e Bayes classi ie a e implemen ed o he
p os a e cance pa ien da a. Compa a i e analysis wi h he help o classi ica ion me ics is
p esen ed. The p edic ion accu acy using decision ees 82% and 79% o Naï e Baye classi ie .
Keywo ds: Deep Lea ning, Pa ien Managemen , Image Segmen a ion, Oncology, P os a e cance
In oduc ion:
An in oduc ion o machine lea ning
based classi ica ion algo i hms in oncological
diseases aims a building e icien and accu a e
p edic i e modelling ools. These ools will
help no only o p edic he isk le el o
se e i y o cance s bu also help in clinical and
adia ion oncology managemen [11] [14].
P edic i e ools designed using ML echniques
p o ide an op imal me hod in he apeu ic and
cance pa ien managemen .
New incidences o male p os a e
cance a e signi ican ly inc easing ac oss he
globe. I anks among he majo cance ype
ound in men. The Popula ion Based Cance
Regis y (PBCRs) and Hospi al Based Cance
Regis y (HBCRs) which a e aimed o keep
eco ds o all cance incidences es ima ed ha
he p os a e cance incidences and dea hs a e
inc easing a a as e a e. The da a published
by In e na ional Agency on Cance esea ch
(IARC) shows ha 19292789 new cance
incidences o all ypes a e epo ed in 2020
[1][3]. This da a also shows ha new 1414259
p os a e cance incidences epo ed which a e
mo e han epo ed in yea 2018 [2] [3].
India epo ed signi ican ise in he
numbe o cance incidences in all egions o
IJAAR Vol. 6 No. 38 ISSN – 2347-7075
Ms. Radhika Bhis e
351
he coun y. A o al o 13324413 new cance
incidences o all ypes o cance s a e epo ed
wi h 851678 dea hs cases. Incidences o new
p os a e cance pa ien s also inc ease in majo
ci ies o an India. The e a e 34540 new
P os a e cance pa ien s epo ed in 2020.
These pa ien s a e mo e han ha o bladde
21096, Thy oid 20432, Gallbladde 19570 and
12642 Panc eas cance pa ien s [6]. P os a e
cance is a second leading cance disease in
Pune, Delhi, Thi u anan hapu am and Kolka a
and hi d in ci ies like Mumbai and Bangalo e
[5]. P os a e cance anks 12 h in all cance
ypes ound in an India [6].
P os a e Gland Ana omy and P os a e
Cance :
Rep oduc i e sys em o male has
seminal esicles, Bulbou e h al and P os a e
accesso y glands. These glands a e esponsible
o anspo a ion o semen and p oduc ion o
s uc u al p o ein and alkaline solu ion ha a e
needed in he o ma ion o spe ma opho e [7].
P os a e gland size changes wi h an
inc ease in men age. The p os a i is condi ion
in p os a e gland occu s by in ec ion. When
gland cell size changes ab up ly, p os a e
cance begins o o m. This cance sp eads
e y slowly and s a s causing symp oms o
yea s and yea .
Fig.1. No mal and Cance P os a e Gland
Digi al ec um examina ion [10],
p os a e speci ic an igen [9] Imaging es and
p os a e biopsy es s a e used o possible
cance symp oms examina ion and diagnosis
in male pa ien s.
T adi ional p os a e cance es ing
ools some imes become less accu a e and
digi al ec um analysis and biopsy may cause
in ec ion, bleeding and pain due o ex e nal
ins umen use.
The new app oach has been
in oduced in cance diagnosis using ad anced
adiog aphic es s and machine lea ning
echniques. Wi h he incu sion o a i icial
in elligence echniques in spine ela ed
su ge y, new app oach has been adop ed o
c ea e clinical analy ics ools and diagnos ic
ins umen s. ML and AI echniques make
clinical p edic ions and decision-making asks
easie [12] [13]. These echniques also play an
impo an ole in ans o ming medical
imaging which inco po a es medical
adiology.
Newly designed diagnos ic imaging
sys ems a e u ilizing he capabili ies o AI and
ML echniques. Specially designed Algo i hms
help pe o mance uning o imaging sys ems
[4-9] and acili a e o quan i y and de ec
a ious possible clinical scena ios.AI
echniques no only show imp essi e
pe o mances in accu a ely iden i ying he
cance egion in p os a e gland in male
ep oduc i e sys em.
Machine Lea ning In P os a e Cance
De ec ion:
Resea che s wi h he help o la es
machine lea ning echniques a e de eloping
ad ance me hods o de ec ion and analyzing
p os a e cance [24].
ML echniques can be bes sui ed wi h
imaging es s ha a e used o p os a e cance
diagnosis. Radiog aphy such as CT and MRI
a e he x- ay imaging es ing me hods ha
easily de ines all p os a e gland ana omical
de ails [23]. This me hod p o ides de ails
IJAAR Vol. 6 No. 38 ISSN – 2347-7075
Ms. Radhika Bhis e
352
abou lesion loca ion, s uc u al issue changes
in gland and size o he p os a e gland.
Con e sion o da a samples in o i s
s a is ical p obabili ies, sigmoid unc ions a e
used in logis ic eg ession gi en by
A. Decision ee Classi ie :
Selec ion o node oo
Finding he key a ibu e om he
gi en da a using unc ion A ibu e
selec ion Measu e (ASM)
Di iding he main node in o subse
Gene a e decision ee node
Repea s ep o main node di ision
B. Naï e Bayes Classi ie :
Con e sion o gi en da a in o
equency able
P obabili ies calcula ion om gi en
da a samples
Bayes heo ems calcula e he pos e io
p obabili ies
P oposed Wo k:
An aim o he p oposed pape is o
implemen he ou ML based classi ie s o
p edic ion o he p os a e cance ype om he
gi en p os a e cance pa ien da a. The cance
da a is CSV based ile ha has in o ma ion
abou p os a e cance esul s ha a e ei he
malignan o benign and geome ic
in o ma ion abou cance p os a e gland.
Decision ee and Naï e Bayes
classi ie s a e implemen ed o p edic he
esul s based on he in o ma ion p o ided in
he gi en da a.
Following essen ial s eps pe o med
o he p edic ion p ocess using decision T ee
and Naï e Bayes Classi ie echniques
Load he da ase
Fea u e ex ac ion om he gi en
da ase
Da a collec ion o aining and es ing
pu poses
T ain he model on he aining da a
P edic ion on he basis o es ing da a
Obse a ions:
The alues o classi ica ion me ics
a e calcula ed by implemen ing he p oposed
classi ie s o he p os a e cance p edic ion.
The p os a e cance esul is ei he Benign o
Malignan . The benign p os a e cance is
ep esen ed by 0 and malignan by 0. The
alues o he me ics o each classi ie a e
gi en in espec i e ables.
Table 1: Classi ica ion Me ics o Decision T ee Classi ie
Accu acy
P ecision
Recall
F1-Sco e
82%
+Ve
-Ve
Sensi i i y
Speci ici y
+Ve
-Ve
0.75
0.92
0.94
0.74
0.83
0.80
Fig 2 Con usion Ma ix o Decision T ee Classi ie
IJAAR Vol. 6 No. 38 ISSN – 2347-7075
Ms. Radhika Bhis e
353
Table 2: Classi ica ion Me ics o Naï e bayes Classi ie
Accu acy
P ecision
Recall
F1-Sco e
79%
+Ve
-Ve
Sensi i i y
Speci ici y
+Ve
-Ve
0.89
0.67
0.83
0.76
0.82
0.74
Fig 3 Con usion Ma ix o Naï e Bayes Classi ie
Conclusion:
The compa a i e analysis be ween
Decision T ee and Naï e Bayes based ML
classi ie s is p esen ed. The nume ical da a
ela ed o p os a e cance gland is used o his
compa a i e analysis. The accu acy o
Decision ee classi ie (DT), KNN and Nai e
Bayes (NB) a e 82%, 73% espec i ely. The
abili y o cap u e all posi i e samples called
T ue Posi i e Ra e (TPR) o sensi i i y o
gi en classi ie s DT and NB a e73% and 83%
espec i ely. Thus, he p os a e cance
p edic ion can be e icien ly pe o med by
using decision ee echnique.
Acknowledgmen :
I would like o exp ess my g a i ude
o D . Mohan Waman P incipal, D . D Y Pa il
A s, Comme ce and Science College Aku di
Pune o aluable guidance.
Re e ences:
1. Hyuna Sung, Jacques Fe lay, Rebecca
L. Siegel, Ma hieu La e sanne,
Isabelle Soe joma a am, Ahmedin
Jemal; ―Global Cance S a is ics 2020:
GLOBOCAN Es ima es o Incidence
and Mo ali y Wo ldwide o 36
Cance s in 185 Coun ies‖; CA: A
Cance Jou nal o Clinicians, Volume
71, Issue 3, 04 Feb ua y 2021
2. F. B ay, J. Fe lay, I. Soe joma a am,
R. L. Siegel, L. A. To e, A. Jemal;
―Global cance s a is ics 2018:
GLOBOCAN es ima es o incidence
and mo ali y wo ldwide o 36
cance s in 185 coun ies‖; CA: A
Cance Jou nal o Clinicians, Volume
70, Issue 4, 06 Ap il 2020
3. In e na ional Agency o Resea ch on
Cance and Global Cance
Obse a o y; h ps://gco.ia c. /
4. K ishnamoo hy Ha iha an,
Venugopal Padmanabha;
―Demog aphic and Disease
cha ac e is ics o p os a e cance in
India‖; Indian Jou nal o u ology,
Volume 32, Issue 2, Pages: 103-108,
2016
5. P ameesha Pe e aa, Ayam Gup ac,
Nidhi Shuklab, Mam a Neh ac,
Mukesh Sha mad, Sneha Mish ad
IJAAR Vol. 6 No. 38 ISSN – 2347-7075
Ms. Radhika Bhis e
354
,Vik am Singh Chauhand, e al; ―
Re isi ing P os a e Cance in India‖;
P ep in s , 26 No embe 2018
6. P ashan Ma hu , K ishnan
Sa hishkuma , Meesha Cha u edi,
P iyanka Das, e al; ―Cance S a is ics,
2020: Repo F om Na ional Cance
Regis y P og amme, India‖; Global
Oncology, Ame ican Socie y o
Clinical Oncology, 16 July 2020
7. Omesh Singh, S ini asa Rao Bolla;
―Ana omy, Abdomen and Pel is,
P os a e‖; S a Pea ls Publishing,
Janua y 2021
8. Anil Bha sa , Sadhna Ve ma;
―Mul ipa ame ic MRI in P os a e
Cance ‖; BioMed Resea ch
In e na ional, ol. 2014, A icle ID
728539, 9 pages, 2014
9. Michael Obana, Hen y O'Law ence;
―P os a e Cance Sc eening: PSA Tes
awa eness among adul males‖; J
Heal h Hum Se Adm. 2015
10. Leen Naji, Ha kanwal Randhawa,
Zah a Sohani; ―Digi al Rec al
Examina ion o P os a e Cance
Sc eening in P ima y Ca e: A
Sys ema ic Re iew and Me a-
Analysis‖; Analysis o Family
Medicine, 149-154, Ma ch
11. Mohammed AliKazem; ―P edic i e
models in cance managemen : A
guide o clinicians‖; Else ie , The
Su geon, Volume 15, Issue 2, Pages
93-97, Ap il 2017
12. Vincenzo Valen ini, Nicola Dinapoli,
And ea Damiani; ―The u u e o
p edic i e models in adia ion
oncology: om ex ensi e da a mining
o eliable modeling o he esul s‖;
Fu u e Oncology, Ma ch 2013
13. De ek J Van Boo en, Manish
Kuchakulla, Ragha Pai, Fabio S
F ech, Reshna Ramasahayam, P i ika
Reddy, Madhumi a Pa ma , Ranji h
Ramasamy, Himanshu A o a1; ―A
Sys ema ic Re iew o A i icial
In elligence in P os a e Cance ‖;
Do ep ess Jou nal, Resea ch and
Repo s in U ology, 2021
14. Ronan Thenaul , Ke in
Kaulanjan,Thomas Da de, Na halie
Rioux Lecle cq, Ka im Bensalah,
Ma ie Me mie , Zine-eddine Khene,
Benoi Pey onne , Shah okh Sha ia ,
Benjamin P ade e , Romain Ma hieu;
―The Applica ion o A i icial
In elligence in P os a e Cance
Managemen —Wha Imp o emen s
Can Be Expec ed? A Sys ema ic
Re iew‖; Mdpi, Applied Science,
2020
15. Lidia Alcala Ma a , Juan An onio
Re ame o, Rajan T. Gup a, Robe o
Ga cía Figue as, An onio Luna;
―A i icial In elligence–assis ed
P os a e Cance Diagnosis:
Radiologic-Pa hologic Co ela ion‖;
RadioG aphics, Vo1ume 41, Numbe
6, 1 Oc obe 2021
16. E. Checcucci, S. Rosa i, S. De Cillis,
e al; ― A i icial in elligence o a ge
p os a e biopsy ou comes p edic ion
he po en ial applica ion o uzzy
logic‖; P os a e Cance and P os a ic
Diseases, 3 Sep embe 2021
17. Tobias Penzko e , Anwa R. Padhani,
Ba is Tu kbey, Masoom A. Haide ,
Henkjan Huisman, Jochen Walz,
Geo g Salomon, I o G. Schoo s,
Jona han Richenbe g, Gee Villei s,
Vale ia Panebianco, Oli ie Rou ie e,
Vibeke Be g Logage , Jelle Ba en sz;
―ESUR/ESUI posi ion pape :
de eloping a i icial in elligence o
p ecision diagnosis o p os a e cance
IJAAR Vol. 6 No. 38 ISSN – 2347-7075
Ms. Radhika Bhis e
355
using Magne ic Resonance Imaging‖;
Sp inge Link, Eu opean Radiology,
31, 9567-9578, 15 May 2021
18. B. M. Zeeshan Hameed, Milap Shah,
Ni hesh Naik, Su yan Ib ahim,
Bhaska Somani, Pa ick Rice, Naeem
Soom o, Bha an P asad Rai;
―Con empo a y applica ion o
a i icial in elligence in p os a e cance
: an i-TRUE s udy‖; SAGE Jou nal,
The apeu ic Ad ances in U ology,
Volume 31, 23 Janua y 2021
19. Chalida Aphini es, Po cha i
Aphini es; ―A i icial in elligence
de elopmen o de ec ing p os a e
cance in MRI‖; Sp inge Open,
Egyp ian Jou nal o Radiology and
Nuclea Medicine, Volume 52, A icle
numbe 87, 26 Ma ch 2021
20. Elle y Wulczyn, Kunal Nagpal,
Ma hew Symonds, Melissa Mo an,
Ma kus Plass, e al; ―P edic ing
p os a e cance speci ic-mo ali y wi h
a i icial in elligence-based Gleason
g ading‖; Communica ion Medicine,
Volume 1, A icle numbe 10, 30 June
2021
21. Kons an ina Kou ou, Kons an ina P.
Exa chos, Cos as Papaloukas,
P odomos Sakaloglou; ―Applied
machine lea ning in cance esea ch:
A sys ema ic e iew o pa ien
diagnosis, classi ica ion and
p ognosis‖; Else ie , Compu a ional
and S uc u al Bio echnology Jou nal,
Volume 19, Pages 5546-5555, 2021
22. Jaspe J. Twil , Kicky G. an
Leeuwen , Henkjan J. Huisman ,
Ju gen J. Fü e e and Maa en de
Rooij; ―A i icial In elligence Based
Algo i hms o P os a e Cance
Classi ica ion and De ec ion on
Magne ic Resonance Imaging: A
Na a i e Re iew‖; Mdpi, Diagnos ic,
11, 959, 26 May 2021
23. Ei ini Polyme i, Hen ik KjolhedeIcon,
Olo Enq is , Johannes Ulen, Mads H.
Poulseng , e al; ―A i icial
in elligence-based measu emen s o
PET/CT imaging bioma ke s a e
associa ed wi h disease-speci ic
su i al o high- isk p os a e cance
pa ien s‖; Scandina ian Jou nal o
U ology, 25 Sep embe 2021
24. Michela An onelli, Edwa d W.
Johns on, Nikolaos Dikaios; ―Machine
lea ning classi ie s can p edic
Gleason pa e n 4 p os a e cance wi h
g ea e accu acy han expe ienced
adiologis s‖; Eu opean adiology,
2019
25. Jill S Ba nhol z-Sloan, Xiaowei Guan,
Cha ni a Zeigle -Johnson, Neal J
Me opol, Timo hy R Rebbeck;
―Decision ee-based modeling o
and ogen pa hway genes and p os a e
cance isk‖; Cance Epidemiol
Bioma ke s P e ., June 2011
26. Li Zhang, Longchao Li, Min Tang, Yi
Huan, Xiaoling, Zhang, Xia Zhe; ―A
new app oach o diagnosing p os a e
cance h ough magne ic esonance
imaging‖; Alexand ia Enginee ing
Jou nal, Volume 60, Issue 1, Pages
897-904, Feb ua y 2021
