Jou nal o Theo e ical and Applied In o ma ion Technology
31s May 2025. Vol.103. No.10
© Li le Lion Scien i ic
ISSN: 1992-8645 www.ja i .o g E-ISSN: 1817-3195
4268
SQUIRREL SEARCH GRADIENT OPTIMIZED DEEP BELIEF
NETWORK CLASSIFIER FOR THYROID DISEASE
PREDICTION
R.VANITHA1, D .K. PERUMAL2
1 Resea ch Schola , Depa men o Compu e Applica ions, Madu ai Kama aj Uni e si y, Madu ai, India
2 P o esso , Depa men o Compu e Applica ions, Madu ai Kama aj Uni e si y, Madu ai, India
E-mail: 1 ani hachezian200[email p o ec ed], 2 pe uma[email p o ec ed]m
ABSTRACT
Thy oid disease is a ange o diso de s ha a ec he hy oid gland, a bu e ly-shaped o gan loca ed in he
neck esponsible o p oducing ho mones ha egula e me abolism, ene gy le els, and o e all bodily
unc ions. Ea ly de ec ion and managemen o hy oid disease a e c ucial, as un ea ed condi ions leads o
se e e complica ions, including ca dio ascula issues, in e ili y, and me abolic diso de s. Ad anced
diagnos ic me hods, including machine lea ning and deep lea ning echniques, a e inc easingly used o
imp o e he accu acy and imeliness o hy oid disease de ec ion, acili a ing be e ea men ou comes. Bu ,
se e i y o hy oid disease p edic ion accu acy wi h minimal ime is majo challenging issues. In o de o
imp o e he accu acy o hy oid disease p edic ion, a no el Squi el Sea ch G adien Op imized Deep Belie
Neu al Classi ie (SSGODBNC) model is de eloped wi h minimal ime consump ion. The p oposed Deep
Belie Ne wo k (DBN) is a ully connec ed a i icial eed- o wa d deep lea ning me hod comp ising wo
isible laye s such as he inpu and ou pu laye and mul iple hidden laye s o p ocessing he gi en inpu . In
he laye -by-laye p ocess, he i s hidden laye ecei es weigh ed inpu and pe o ms da a p ep ocessing.
Then ex ac ing signi ican ea u es and elimina es he insigni ican ea u es om he da ase using he Spa se
Au oencode model. These selec ed signi ican ea u es a e u ilized o classi y he se e i y le el o hy oid
disease using Sokal–Michene ’s simple ma ching me hod. Du ing ine- uning, e o back-p opaga ion
algo i hms adjus he hype pa ame e s using Squi el Sea ch G adien Op imiza ion o inc ease he accu acy
o hy oid disease classi ica ion. This op imized ine- uning p ocess signi ican ly enhances he pe o mance
o he deep belie ne wo k and imp o es o e all lea ning e iciency in classi ica ion asks. Finally, he
accu a e hy oid disease se e i y p edic ion esul s wi h minimal e o a e ob ained a he ou pu laye .
Expe imen al assessmen is conduc ed wi h di e en e alua ion me ics such as Accu acy, P ecision, Recall,
F1-sco e, speci ici y and Thy oid disease p edic ion ime. The obse ed esul shows he e ec i eness o he
p oposed SSGODBNC model wi h highe accu acy in hy oid disease p edic ion wi h minimum ime han
he exis ing me hods.
Keywo ds: Thy oid Disease P edic ion, Deep Belie Ne wo k, Fine-Tuning, Adap i e G adien Me hod,
Squi el Sea ch G adien Op imiza ion, Sokal–Michene ’s Simple Ma ching Me hod.
1. INTRODUCTION
Thy oid disease poses a subs an ial heal h
isk, nega i ely impac ing an indi idual's quali y
o li e while also leading o inc eased medical
expenses o diagnosis and ea men . Iden i ying
hy oid disease pa icula ly challenging,
especially o less expe ienced heal hca e
p o essionals, as i s symp oms o en o e lap wi h
hose o o he condi ions. Recen ad ancemen s
in medical esea ch ha e highligh ed he po en ial
o machine lea ning echniques as e ec i e ools
o diagnosing diseases. By analyzing pa e ns in
clinical da a, machine lea ning models suppo s
p ac i ione s in making accu a e and imely
diagnoses, he eby imp o ing pa ien ou comes and
educing he bu den on heal hca e sys ems.
The scopes o he DBNs sugges he po en ial
pa h o p edic ing hy oid diseases, wi h he aid o
ea ly diagnosis and modi ied ea men . I s con ex
ex ends o enhance he accu acy and e iciency in
iden i ying di e en hy oid condi ions. The DBNs
can be ained o o ecas he en i y pa ien esponses
o a ious ea men s, allowing o u he
pe sonalized and managemen o hy oid diso de
and u ilized o de e mine he medical images such as,
X- ays and CT scans o hy oid abno mali ies. I
Jou nal o Theo e ical and Applied In o ma ion Technology
31s May 2025. Vol.103. No.10
© Li le Lion Scien i ic
ISSN: 1992-8645 www.ja i .o g E-ISSN: 1817-3195
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classi ies and diagnoses hy oid condi ions based
on hy oid scin ig aphy images, o e ing ano he
ool o diagnosis.
A Dynamic Selec ion Hyb id Model
(DSHM) was p oposed in [1] o p edic hy oid
disease and imp o e accu acy h ough obus
ea u e selec ion. Howe e , he DSHM model
equi es highe compu a ional ime o hy oid
disease p edic ion. A S acked Ensemble wi h IG
ea u e selec ion model was designed in [2] wi h
he aiming o enhance hy oid disease de ec ion
and educe sc eening ime and cos s conside ing
ew clinical a ibu es. Bu i ailed o p edic he
se e i y le el o hy oid disease p edic ion.
Di e en machine lea ning models we e
de eloped in [3] o de ec ing hy oid disease,
inco po a ing a di e en ial e olu ion (DE)-based
op imiza ion algo i hm o ine- une pa ame e s and
minimize e o s. Bu , i did no add ess inc easing
he da ase size, limi ing he abili y o u he
analyze he pe o mance o deep lea ning models.
A gene alized deep lea ning-based decision
suppo sys em was p oposed in [4] o imp o e
hy oid cance diagnosis and enhance o e all
diagnos ic pe o mance. Howe e , challenges
ela ed o p ecision and ecalls in hy oid cance
de ec ion emain un esol ed. To enhance he
pe o mance o p ecision and ecall, a no el
andom o es -based sel -s acking classi ie model
was de eloped in [5] o e icien hy oid disease
de ec ion. Howe e , he ime complexi y o
hy oid cance p edic ion emained unadd essed.
Va ious machine lea ning app oaches we e
de eloped in [6] o p edic ing papilla y hy oid
cance . Howe e , deep lea ning models we e no
u ilized o enhance he accu acy o cance
p edic ion while minimizing ime consump ion. A
Quan um Suppo Vec o Machine classi ie model
was de eloped in [7] o mo e accu a e
classi ica ion o hy oid cance by selec ing
signi ican ea u es using he Quan um Pa icle
Swa m Op imiza ion me hod. Howe e , i ailed o
apply e ec i e ea u e selec ion and classi ica ion
algo i hms o imp o e he pe o mance o hy oid
disease p edic ion and achie e be e accu acy
a es.
Se e al machine lea ning echniques
we e p oposed in [8] o classi ying hy oid disease
p edic ions, which include da a p epa a ion,
ea u e selec ion, and hype pa ame e uning.
Howe e , hese me hods did no add ess he
educ ion o ime complexi y in hy oid disease
p edic ion. A obus and e ec i e machine lea ning-
based me hod was de eloped in [9] o p edic ing
hy oid disease by add essing class imbalance and
pe o ming ea u e selec ion. Howe e , a ious
ea u e selec ion echniques and obus handling o
missing da a we e no adequa ely add essed. A ine-
uned Ligh G adien Boos ing Machine (LGBM)
model was de eloped in [10] o achie e high accu acy
in hy oid disease p edic ion. Howe e , i did no
inco po a e deep lea ning models o u he enhance
he diagnos ic pe o mance o hy oid disease.
Di e en machine lea ning algo i hms we e designed
in [11] o p edic hypo hy oidism and hype hy oidism
by iden i ying he mos signi ican ea u es o
dis inguish hy oid diseases mo e accu a ely.
Howe e , i ailed o de elop a mo e e ec i e ea u e
selec ion scheme o u he imp o e he esul s.
An ensemble lea ning model was de eloped
in [12] o au oma ic, eliable, and accu a e hy oid
ecogni ion wi h he aim o imp o ing p edic ion
accu acy. Howe e , i ailed o cons uc a mul iclass
hy oid classi ica ion model. A h ee-s age hyb id
classi ie (3SHC) model was de eloped in [13] o
disease p edic ion by educing he da ase dimension
and pe o ming ea u e selec ion. Bu , he designed
classi ie model equi es highe compu a ional
esou ces and esul s in inc eased compu a ional cos s.
An op imized ex eme g adien boos ing mul iclass
classi ie model was in oduced in [14] o classi y
pa ien s wi h di e en ypes o hy oid disease.
Howe e , sophis ica ed deep lea ning models ha e no
been applied o achie e e en mo e accu a e and
e ec i e ou comes. The eg esso and classi ie model
de eloped in [15] aimed o p edic he occu ence o
hypo hy oidism by analyzing he ea u es equi ed o
classi ica ion. Howe e , i ailed o op imize he model
hype pa ame e s o minimize he s a is ical loss
unc ions.
1.1 A No el Con ibu ion o The SSGODBNC
Me hod
The majo con ibu ions o he SSGODBNC
model is lis ed as ollows,
 To enhance he accu acy o hy oid disease
p edic ion, he SSGODBNC model has been
de eloped, inco po a ing p ep ocessing, da a
ea u e selec ion, and classi ica ion.
 A no el y o deep lea ning model pe o ms
da a p ep ocessing and ea u e selec ion in he
hidden laye using SSGODBNC model
designed in o minimize he aining ime o
hy oid disease p edic ion
Jou nal o Theo e ical and Applied In o ma ion Technology
31s May 2025. Vol.103. No.10
© Li le Lion Scien i ic
ISSN: 1992-8645 www.ja i .o g E-ISSN: 1817-3195
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 A no el me hod o Sokal–Michene ’s
simple ma ching echnique is de eloped o
analyzing he aining and es ing da a
samples and p o ides he mul i class
classi ica ion ou come o enhance he
accu acy wi h minimum e o .
 A no el y o squi el Sea ch Algo i hm is
ob ained in ine- uning p ocess o op imize
he e o a e and imp o e he accu acy o
hy oid disease p edic ion.
 Finally, an expe imen al e alua ion is
ca ied ou o es ima e he pe o mance o
he SSGODBNC model using a ious
me ics and compa ing i o o he
classi ica ion me hods.
1.2 P oblem S a emen
The hy oid disease p edic ion is imp o ing
he occu ence o hy oid diso de s and equi e o
co ec and ea ly de ec ion o enhance he pa ien
esul s and minimum heal hca e cos s. The hy oid
disease p edic ion [1] designed o enhance
accu acy by obus ea u e selec ion. Bu , he
DSHM model was no educing he compu a ional
ime. The ea u e selec ion model [2] in oduced
wi h imp o ed hy oid disease de ec ion wi h
lesse sc eening ime and cos s. Howe e , he
se e i y le el o hy oid disease p edic ion was no
de e mined. The di e en machine lea ning
me hods a e designed in [6] o hy oid cance . Bu ,
i ailed o imp o e accu acy o cance p edic ion
wi h educed ime consump ion. To o e come his
issue, he p oposed SSGODBNC model achie ed
wi h be e accu acy in hy oid disease p edic ion
wi h lesse ime han he exis ing me hods.
1.3 O ganiza ion
The pape is s uc u ed as ollows:
Sec ion 2 p o ides a e iew o ela ed wo ks in he
ield, highligh ing issues. Sec ion 3 in oduces he
p oposed SSGODBNC model, o e ing a de ailed
explana ion along wi h a diag am o be e
unde s anding. Sec ion 4 ou lines he expe imen al
se up and p o ides a desc ip ion o he da ase used
o e alua ion. In Sec ion 5, he pe o mance o he
p oposed model is compa ed wi h exis ing
me hods, conside ing a ious pa ame e
con igu a ions. Finally, Sec ion 6 p esen s he
conclusion.
2. RELATED WORKS
A Ligh G adien Boos ing Classi ie
model was de eloped in [16] o achie e high
pe o mance in hy oid cance diagnosis. Howe e ,
he designed model ailed o be e ec i ely applied in
clinical p ac ice o imp o e i s p edic i e accu acy. A
new combina ion o K-Neighbo s (KN) and Random
Fo es (RF) classi ie models was de eloped in [17]
o he e ec i e iden i ica ion o hy oid synd ome.
Howe e , i did no inco po a e mo e ad anced neu al
ne wo k-based app oaches o u he enhance he
pe o mance sco es o hy oid synd ome de ec ion.
An ensemble machine lea ning classi ie model was
in oduced in [18] o imp o e classi ica ion
pe o mance wi h highe speci ici y. Bu , i ailed o
ex end he classi ica ion o hy oid disease using an
explainable machine lea ning app oach, which could
enhance accu acy, anspa ency, and ou comes. A
machine lea ning (ML) in eg a ion was de eloped in
[19] by applying mul i-c i e ia decision-making o
hy oid p edic ion. Howe e , he ime complexi y o
he hy oid p edic ion was highe . A hyb id model
combining ensemble s acking and an ad anced ea u e
selec ion echnique was de eloped in [20] o enhance
he accu acy o hy oid diso de de ec ion. Howe e ,
he pe o mance o sensi i i y analysis in hy oid
diso de de ec ion was no add essed. An in e p e able
hy oid ca ego iza ion app oach was in oduced in
[21] using explainable AI, achie ing he highes
accu acy pe o mance. Howe e , i did no apply a
mul iclass classi ica ion app oach.
A andom o es model was de eloped in [22]
o achie e imp o ed p edic ion pe o mance o
hy oid papilla y cance . Howe e , he issue o ime
consump ion in p edic ing hy oid papilla y cance
emained un esol ed. A machine lea ning app oach
was de eloped in [23] o p edic di e en ia ed hy oid
cance based on hype pa ame e uning. Howe e , i
ailed o explo e hese models on la ge and mo e
di e se da ase s o alida e he hy oid cance
p edic ion. In [24], machine lea ning algo i hms we e
designed o p edic medulla y hy oid ca cinoma.
Howe e , op imiza ion and addi ional p edic i e
ac o s we e no inco po a ed in o he ca cinoma
p edic ion. A con olu ional neu al ne wo k (CNN)
p edic ion model was de eloped in [25] wi h he aim
o de ec ing papilla y hy oid cance , achie ing high
sensi i i y and speci ici y. Bu , an e icien
op imiza ion model was no applied o u he enhance
he hy oid cance p edic ion. A machine lea ning
model using eXplainable A i icial In elligence (XAI)
was in oduced in [26] o imp o e hy oid disease
p edic ion. Howe e , mul i-label hy oid disease
p edic ion emained unadd essed. Risk p edic ion
models we e de eloped in [27] wi h he aim o
p edic ing he ce ical lymph node in ol emen in
papilla y hy oid ca cinoma. Howe e , he models
Jou nal o Theo e ical and Applied In o ma ion Technology
31s May 2025. Vol.103. No.10
© Li le Lion Scien i ic
ISSN: 1992-8645 www.ja i .o g E-ISSN: 1817-3195
4271
ailed o alida e he e icacy o hese p edic ion
models.
An e icien nomog am model was
de eloped in [28] by u ilizing isualized
mul ipopula ion da a o accu a ely classi y hy oid
ca cinoma. Bu , a deep lea ning classi ie model
was no applied o enhance he accu acy o hy oid
ca cinoma p edic ion. A bina y logis ic eg ession
and Lasso eg ession model was de eloped in [29]
o a iable selec ion and isk ac o analysis in
hy oid disease p edic ion. Howe e , he e o a e
in he isk ac o analysis was no e ec i ely
add essed. A no el condi ional gene a i e
ad e sa ial ne wo k model was de eloped in [30]
wi h he aim o de ec ing hy oid disease by
ex ac ing mul i-scale ea u es. Bu , i ailed o
pe o m an in-dep h analysis o hy oid disease
p edic ion.
3. PROPOSAL METHODOLOGY
Thy oid disease is a signi ican cause o
mo ali y, highligh ing he impo ance o ea ly
diagnosis o mi iga e i s impac . Howe e , exis ing
me hods in heal hca e diagnosis ace challenges
ega ding pe o mance consis ency and accu a e
disease p edic ion wi hin minimal ime. This
sec ion in oduces a no el me hodology called
SSGODBNC, de eloped o accu a e hy oid
disease p edic ion. The wo king me hodology o
he SSGODBNC model is di ided in o ou
p ima y p ocesses namely da a acquisi ion, da a
p ep ocessing, ea u e selec ion, and classi ica ion.
Figu e 1 p o ides an o e iew o he en i e
wo king p ocess o he SSGODBNC model.
Figu e 1: A chi ec u e Diag am o SSGODBNC Model
Figu e 1 abo e illus a es he a chi ec u e o
he p oposed SSGODBNC model, which aims o
achie e accu a e hy oid disease p edic ion in medical
da a p ocessing. The SSGODBNC model in eg a es
a ious undamen al p ocesses ha wo k
collabo a i ely o enhance he p edic ion accu acy and
e iciency. These p ocesses include da a
p ep ocessing, ea u e selec ion, and e alua ion, each
playing a c ucial ole in e ining he da ase and
imp o ing he pe o mance o he disease p edic ion
model. Th ough applying op imiza ion, he model
p o ides he be e p edic ion esul s. In he ollowing
subsec ions, each o hese p ocesses is explained in
de ail, highligh ing hei signi icance in he o e all
amewo k o he p oposed model.
3.1 Da a Acquisi ion
Da a acquisi ion is he c ucial s ep in he
SSGODBNC model ha in ol es ga he ing ele an
and eliable da a om he heal hca e da abases namely
Thy oid disease da ase ex ac ed om
h ps://www.kaggle.com/da ase s/emmanuel we / hy
oid-disease-da a. This s ep ensu es ha su icien
in o ma ion is a ailable o ain and alida e he model
e ec i ely. In he con ex o hy oid disease
p edic ion, da a acquisi ion ocuses on collec ing
pa ien - ela ed in o ma ion, including clinical
ea u es, es esul s, and demog aphic de ails, o
c ea e a comp ehensi e da ase o u he p ocessing.
Accu a e and high-quali y da a acquisi ion plays a
i al ole in enhancing he eliabili y and pe o mance
o he p oposed SSGODBNC model.
The da ase includes 9172 ins ances o
eco ds o da a samples and 31 a ibu es o ea u es
o accu a e hy oid disease p edic ion. The 31
a ibu es a e lis ed as ollows, age o he pa ien , sex
o pa ien , on_ hy oxine, que y on hy oxine, on
an i hy oid meds, sick, p egnan , hy oid_su ge y,
I131_ ea men , que y_hypo hy oid,
que y_hype hy oid, li hium, goi e , umo ,
hypopi ui a y, psych, TSH_measu ed, TSH,
T3_measu ed, T3 le el in blood om lab wo k ( loa ),
TT4_measu ed in he blood, TT4 le el in blood,
T4U_measu ed in he blood, T4U le el in blood,
FTI_measu ed , FTI le el in blood, TBG_measu ed ,
TBG, e e al_sou ce, a ge , pa ien _id
Le us conside he da ase ‘𝐷𝑆’ and samples
as well as ea u es a e a anged in he o m o ma ix.
The e o e, he inpu ma ix is o mula ed as gi en
below,
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31s May 2025. Vol.103. No.10
© Li le Lion Scien i ic
ISSN: 1992-8645 www.ja i .o g E-ISSN: 1817-3195
4272
𝐼𝑀 =
⎣
⎢
⎢
⎢
⎡
𝐹𝐹… 𝐹
𝑆 𝑆 … 𝑆
𝑆 𝑆 … 𝑆
⋮ ⋮ … ⋮
𝑆 𝑆 … 𝑆
⎦
⎥
⎥
⎥
⎤
(1)
Whe e, 𝐼𝑀 indica es an inpu ma ix,
each column indica es a numbe o ea u es𝐹 =
{𝐹,𝐹,…,𝐹}, each ow indica es a numbe o
samples o ins ances o eco ds ‘𝑆 =
{𝑆,𝑆,…,𝑆}’ espec i ely.
3.2 P oposed Deep Belie Neu al Ne wo k
The SSGODBNC model employs a Deep
Belie Ne wo k (DBN), a specialized deep lea ning
model designed o enhance he accu acy o hy oid
diseases de ec ion while educing p ocessing ime.
This app oach imp o es he ea u e selec ion and
classi ica ion, especially o handling he la ge
olume o sequen ial da a. A DBN a chi ec u e
consis s o mul iple laye s o Res ic ed
Bol zmann Machines (RBMs) a anged
hie a chically, unc ioning as a gene a i e model.
This laye ed a chi ec u e minimized he
compu a ional complexi y while handling he la ge
olume o da a samples. Addi ionally, he
p oposed deep lea ning a chi ec u e e ec i ely
minimizes e o s du ing aining, leading o
imp o ed o e all pe o mance in he hy oid
diseases p edic ion.
Figu e 2 depic s he a chi ec u e o a
Deep Belie Ne wo k o accu a e hy oid disease
p edic ion. The lea ning p ocess is di ided in o
wo p ima y p ocesses namely laye -by-laye
aining and ine- uning.
Du ing he laye -by-laye aining phase,
each laye o he Deep Belie Ne wo k p ocesses
weigh ed inpu da a samples and ans e ed in o
he nex laye . In he ine- uning phase, e o back
p opaga ion is
Figu e 2 : Cons uc ion o Deep Belie Ne wo k
employed o adjus he ne wo k’s hype pa ame e s, by
applying a Squi el Sea ch G adien Op imiza ion
me hod o enhanced pe o mance.
In he laye -by-laye app oach, Deep Belie
Ne wo ks u ilizes he Res ic ed Bol zmann Machines
(RBMs), which a e s ochas ic neu al ne wo ks.
Res ic ed Bol zmann Machines (RBMs) a e a ype o
s ochas ic neu al ne wo k used o unsupe ised
lea ning, pa icula ly in ea u e ex ac ion and
dimensionali y educ ion asks. An RBM consis s o
wo laye s such as a isible laye and a hidden laye .
The isible laye ep esen s he inpu da a samples,
while he hidden laye p ocesses he da a samples. The
ou pu gene a ed by one RBM se es as he inpu o
he isible laye o he nex RBM, as illus a ed in
Figu e 2.
As exposed in he igu e 2, he DBNs consis
o aining se {𝑆,𝑌} whe e 𝑆 deno es a inpu da a
samples 𝑆={𝑆,𝑆,𝑆,…𝑆}’ collec ed om he
da ase and a label o ou pu ‘𝑌’ ep esen ing i s
ca ego y which belongs o he di e en classes(𝑌∈
1,2,3…𝑘). The inpu da a samples is associa ed o a
weigh ‘ 𝜗,𝜗,…,𝜗’ and added wi h bias ‘𝐵’. The
p obabili y o he neu on ac i a ion in he isible laye
is gi en below,
𝑃 = 𝐹∑𝑆

 ⬚∗𝜗+𝐵 (2)
Whe e, 𝑃 deno es a neu on ac i a ion
p obabili y in isible laye , 𝐹 symbolizes a sigmoid
ac i a ion unc ion, ‘𝑆’ indica es an inpu pa ien da a
samples, 𝜗 ep esen s a weigh s in isible laye 𝐵
indica es a bias o isible laye . I he neu on
ac i a ion p obabili y 𝑃 = 1 , hen he inpu da a

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samples a e sen in o he hidden laye . In ha laye ,
da a p ep ocessing is ca ied ou by signi ican ly
imp o e he pe o mance o p edic i e models by
add essing issues o missing alues in he gi en
da ase .
3.2.1 Da a P ep ocessing
Da a p ep ocessing is essen ial o
ensu ing ha he machine lea ning model lea ns
om high-quali y, s uc u ed, and ele an da ase ,
leading o mo e eliable and accu a e disease
p edic ions. In he p ep ocessing s ep, he
p oposed SSGODBNC model add esses he
missing da a in he gi en da ase h ough he
nea es neighbo impu a ion me hod.
The i s s ep in ol es analyzing he
da ase o ecognize he dis ibu ion o missing
alues. A e ha , he nea es neighbo impu a ion
me hod is hen applied o inding he missing
alues. The alues o he nea es neighbo s a e
a e aged o ill in he missing en ies. The missing
da a impu a ion p ocess is exp essed as ollows,
𝑆 =∑


∑

 (3)
Whe e, 𝑆 indica es a missing da a
alues, 𝑆 deno es an obse ed neighbo ing known
da a sample alues a ailable in da ase , 𝛿
designa es a weigh s assigned o he neighbo ing
known da a sample alues.
A e inding he missing da a, he
de e mined alues a e e ined by applying a
no maliza ion p ocess. I is used o educe he
dimensionali y o he da ase and cap u e he mos
signi ican alues ha explain he a iance in he
da a. This e inemen p ocess s abilizes he e ec
o bo h con inuous and ca ego ical a iables which
cap u e he unde lying s uc u e and ela ionships
wi hin he da a. In his s ep, he mean o each
known alue is compu ed as ollows,
𝜇 = 
∑𝑆

 (4)
Whe e, 𝜇 deno es a mean o each alue,
𝐾 deno es a numbe o neighbo ing da a samples.
A e ha , he no maliza ion me hod is applied o
escales da a in o s anda d no mal dis ibu ion.
𝑆 = ()
 (5)
Whe e,𝑆 deno es a no maliza ion o he
espec i e missing alues ‘𝑆’ and 𝜇 deno es a
mean, 𝜎 deno es a s anda d de ia ion. The missing
alues, impu ed wi h he mean alue o minimize
de ia ion, ensu e ha he unde lying s uc u e o he
da a is accu a ely e lec ed. Finally, hese impu ed
alues e ine he da ase , enhancing he accu acy o
disease p edic ion while minimizing ime
consump ion.
3.2.2 Fea u e Selec ion
A e he p ep ocessing, he ea u e selec ion
p ocess is pe o med o educe i s dimensionali y. This
s ep in ol es iden i ying and e aining he mos
impo an ea u es while disca ding less ele an o
edundan ones. By ocusing on he mos in o ma i e
a ibu es, ea u e selec ion no only simpli ies he
da ase bu also enhances he e iciency and accu acy
o subsequen modeling asks. This p ocess ensu es
ha he model ope a es on a e ined se o ea u es,
educing compu a ional complexi y and imp o ing
p edic i e pe o mance. The p oposed SSGODBNC
model u ilizes he Con e gen P opaga ed Spa se Au o
encode model o selec ing he signi ican ea u es by
emo ing he o he ea u es. The Spa se Au o encode
is a a ia ion o an au o encode neu al ne wo k ha
helps o enhance he lea ning o inpu samples and
p o ides he ou pu in e ms o compac and
meaning ul ep esen a ion.
A Spa se Au o encode pe o ms wo majo
p ocesses namely o wa d p opaga ion and backwa d
p opaga ion o minimize he dimensionali y o he
inpu da ase . The p oposed au o encode model
conside he p ep ocessing ou pu ‘𝑃𝑂’ as inpu o
ea u e selec ion. Fo wa d p opaga ion is used o ind
he mos signi ican ea u e ha maximizes he
objec i e unc ion’ 𝐽’ as exp essed as ollows,
𝐹= 𝑎𝑟𝑔𝑚𝑎𝑥 𝐽𝑂𝑢𝑡+
𝑃𝑂,𝑤ℎ𝑒𝑟𝑒 𝑃𝑂 ∈ 𝑂𝑢𝑡, 𝑃𝑂 ∉ 𝑂𝑢𝑡 (6)
Whe e, 𝐹 ea u es selec ion a he o wa d
p opaga ion, 𝑎𝑟𝑔𝑚𝑎𝑥 deno es a gumen o maximum
unc ion, 𝑂𝑢𝑡 deno es a o wa d p opaga ion o he
ea u es combined wi h he p ep ocessing ou pu ‘𝑃𝑂’
du ing he ea u e selec ion p ocess. A e he o wa d
p opaga ion iden i ies signi ican ea u es, backwa d
p opaga ion is employed o elimina e insigni ican
ea u es om he selec ed se . This ensu es ha he
ea u e se is e ined o e ain only he mos ele an
and impac ul ea u es.
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𝐹= 𝑎𝑟𝑔𝑚𝑎𝑥 𝐽𝑂𝑢𝑡−
𝑃𝑂,𝑤ℎ𝑒𝑟𝑒 𝑃𝑂 ∈ 𝑂𝑢𝑡,𝑃𝑂 ∉ 𝑂𝑢𝑡 (7)
𝐹𝑆 = (𝐹 ,𝐹) (8)
Whe e, 𝐹 ea u es elimina ion a he
backwa d p opaga ion, 𝑎𝑟𝑔𝑚𝑎𝑥 deno es
a gumen o maximum unc ion, 𝑂𝑢𝑡 deno es a
backwa d p opaga ion ou pu combined wi h he
p ep ocessing ou pu ‘𝑃𝑂’ du ing he ea u e
selec ion p ocess, 𝐽 deno es a objec i e unc ion,
𝐹𝑆 indica es a inal dimensionali y ea u e se
ou pu which includes he signi ican ea u e
selec ion and elimina ion. The p ocess con e ges
when he o al numbe o ea u es in he hy oid
disease da ase is ully e alua ed (bo h selec ed and
elimina ed), ensu ing ha he inal ea u e se ‘𝐹𝑆’
con ains only he mos ele an ea u es. This
app oach enhances he model's pe o mance by
op imizing he ea u e se h ough i e a i e
e inemen . The selec ed signi ican ea u es a e
ans e ed in o he hi d hidden laye .
3.2.3 Classi ica ion
A e he ea u e ex ac ion phase, he
classi ica ion p ocess is pe o med o analyze he
ex ac ed ea u es om he aining and es ing
da ase s. This phase is c ucial o building and
alida ing he p edic i e model. The Sokal–
Michene ’s simple ma ching me hod is a s a is ical
me hod which applied o analyze he aining and
es ing da a samples based on he co ela ion
measu e. I is ma hema ically compu ed as
ollows,
𝑐𝑜𝑟𝑟 (𝑆,𝑆)= 1−| ∆ |
 (9)
𝑌 = 𝑐𝑜𝑟𝑟 (𝑆,𝑆) (10)
Whe e, 𝑌 deno es an analysis ou comes,
𝑐𝑜𝑟𝑟 (𝑆,𝑆)indica es a co ela ion be ween he
es ing da a samples ‘𝑆’ and aining samples
‘𝑆’,’ 𝑛’ deno es a numbe o samples,𝑆 ∆ 𝑆
deno es a de ia ion be ween he samples. Based on
he Sokal–Michene ’s simple ma ching me hod,
he co ela ion p o ides he simila i y ou comes
om ‘0’ o ‘1. The maximum co ela ion esul s
p o ide he inal classi ica ion ou comes.
3.2.4 Fine Tuning
Fine-Tuning is a i al p ocess in deep
lea ning whe e a classi ie model is u he op imized
o pe o m a speci ic ask. I is used o e ine he
weigh s o he ne wo k o imp o ed classi ica ion
pe o mance. In ine uning p ocess, he e o a e is
measu ed based on squa ed di e ence be ween he
ac ual and p edic ed classi ica ion ou pu as ollows,
𝐸𝑅 = 𝑌 −𝑌 (11)
Whe e, ‘𝐸𝑅’ symbolizes he e o a e,
𝑌signi ies he ac ual classi ica ion ou pu , 𝑌
symbolizes he p edic ed classi ica ion ou pu . In
o de o minimize he e o , he adap i e G adien
me hod is employed o upda e he weigh .
𝜗 = 𝜗 − 𝜂 󰇣 
󰇤 (12)
Whe e, 𝜗 indica es a new weigh ,
𝜗speci ies a cu en weigh , 𝜂 indica es a lea ning
a e, 
signi ies he i s -o de de i a i e o ind ou
a local minimum o a unc ion (i.e. e o a e) by
upda ing he cu en weigh ‘𝜗’
In o de o ind op imal weigh alue,
Squi el Sea ch Op imiza ion algo i hm is employed
o educe he e o and enhance he accu acy o hy oid
disease p edic ion. Squi el Sea ch Op imiza ion
(SSO) is a me a-heu is ic algo i hm inspi ed by he
adap i e o aging beha io o squi els. Du ing wa m
wea he , squi els ac i ely glide be ween ees in
sea ch o ood esou ces, showcasing dynamic
explo a ion pa e ns. In his op imiza ion algo i hm,
squi els symbolizes weigh s, while ood esou ces
ep esen ed by a i ness unc ion. In colde pe iods,
hei ac i i y dec eases as hey conse e ene gy o
mee hei basic needs. When he wea he becomes
cons uc i e again, he squi els esume hei ac i e
o aging and explo a ion beha io s. This cyclic pa e n
con inues h oughou he li espan o he squi els,
o ming he basis o he op imiza ion p ocess.
Fi s , popula ions o squi els (i.e. weigh s)
a e ini ialized in sea ch space,
𝜗= 𝜗,𝜗,𝜗,….𝜗 (13)
Whe e, 𝜗 deno es a ‘𝑏’ numbe o upda ed
weighs. Fo each squi el (i.e. weigh ), he i ness is
measu ed based on he e o a e.
𝑓(𝜗) = 𝑎𝑟𝑔𝑚𝑖𝑛𝐸𝑅(14)
Whe e,𝑓(𝜗) ep esen s a i ness o weigh ,
𝑎𝑟𝑔𝑚𝑖𝑛 indica es an a gumen o minimum unc ion,
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𝐸𝑅 indica es an e o a e o he classi ie model.
Based on he i ness es ima ion, he cu en bes
weigh is selec ed among he popula ion. Then
execu es a di e en beha io s o he squi els as
ollows,
 New Loca ions Gene a ion Th ough
Gliding
In his beha io , new loca ions a e
gene a ed by mimicking he gliding beha io o
squi els, which e lec s hei na u al o aging
habi s. This gliding mechanism enables he
algo i hm o e icien ly explo e he solu ion space
in de ec ion o op imal esul s.
𝑋 = 𝑋+𝐷𝐺∗
|𝑋−𝑋|
(15)
Whe e, 𝑋 indica es a new loca ion o
he squi els, 𝑋 indica es old loca ion o he
squi el, 𝐷 deno es a andom gliding dis ance, 𝐺
indica es a gliding cons an , |𝑋−𝑋| indica es
a de ia ion be ween he cu en posi ion o squi el
‘𝑋’ and 𝑋’ indica es a bes posi ion o he
squi el.
 Ve i y Seasonal Moni o ing Condi ion
The o aging pa e ns o lying squi els
a e signi ican ly in luenced by seasonal a ia ions.
To add ess his, a seasonal moni o ing mechanism
is implemen ed, ensu ing he algo i hm a oids
becoming s uck in local op ima.
𝑍𝑆 = (𝑋− 𝑋) (16)
Whe e, 𝑍𝑆 deno es a seasonal cons an ,
𝑋 indica es a cu en solu ion, 𝑋 designa es an
bes posi ion o squi el.
𝑍𝑆 = []
󰇡 
󰇢∗. (17)
Whe e𝑍𝑆indica es a minimum
seasonal cons an , 𝐼𝑡𝑒𝑟indica es an i e a ion,
𝐼𝑡𝑒𝑟designa es a maximum i e a ion.
When 𝑍𝑆 < 𝑍𝑆 indica ing he end o win e ,
lying squi els lose hei abili y o na iga e he
o es e icien ly and ins ead begin andomly
explo ing new loca ions in sea ch o ood. This
cycle con inues un il he maximum numbe o
i e a ions is achie ed. I no , he p ocess o
gene a ing new posi ions and e alua ing seasonal
moni o ing condi ions is epea ed.
Figu e 3 : Flow Cha o Squi el Sea ch Op imiza ion
Figu e 3 demons a es he low diag am o
he squi el sea ch op imiza ion o selec ing he
op imal weigh wi h minimum e o . As a esul , hen
he op imally selec ed weigh s a e used o enhance he
disease p edic ion. Finally, he disease p edic ion
esul s a e ob ained a he ou pu laye as ollows,
𝑌 = 𝐹 ( 𝛿ℎ∗ ℎ) (18)
Whe e 𝑌 indica es a mul iclass
classi ica ion ou pu , 𝐹 indica es a so max
ac i a ion unc ion, ℎ indica es an ou pu o he
p e ious hidden laye , 𝛿 deno es a weigh be ween
he hidden and ou pu laye . A so max ac i a ion
unc ion ‘𝐹’ in he ou pu laye o mul i class
classi ica ion ou pu is o mula ed as ollows.
𝐹 =()
∑()

 (19)
F om he abo e (19), he so max ac i a ion
unc ion is used o make a mul iple classi ica ion
esul s, 𝑌 deno es a aw ou pu o he 𝐾 class, 𝐶
deno es a o al numbe o classes. The algo i hmic
p ocess o Squi el Sea ch G adien Op imized Deep
Belie Neu al Classi ie model is gi en below.
// Algo i hm 1: Squi el Sea ch G adien
Op imized Deep Belie Neu al Classi ie model
Inpu : da ase ‘
𝐷𝑆
’ , numbe o ea u es
𝐹
=
{
𝐹

,
𝐹

,
…
,
𝐹

}
, samples ‘
𝑆
=
{
𝑆

,
𝑆

,
…
,
𝑆

}
’
Ou pu : Inc ease he disease p edic ion accu acy
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31s May 2025. Vol.103. No.10
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4276
Begin
1. Collec numbe o ea u es
𝐹
=
{𝐹,𝐹,…,𝐹} and samples ‘𝑆 =
{𝑆,𝑆,…,𝑆}--- inpu laye
2. Fo each sample 𝑆
3. Fo mula e he neu on ac i a ion
p obabili y using (1)
4. End Fo
5. P ep ocessing he da a samples using
(3) (4) (5)–[hidden laye 1]
6. Fo each p ep ocessing samples ‘𝑷𝑶’
7. Pe o m o wa d p opaga ion o ex ac
signi ican ea u es as gi en in (6)
8. Pe o m backwa d p opaga ion p ocess
o elimina e insigni ican ea u es (7)
9. Ob ain he ea u e se ‘FS’ using (8)
10. End o
11. End o
12. Fo each aining and es ing da a
samples
13. Apply Sokal–Michene ’s simple
ma ching me hod using (9)
14. Ob ain he classi ica ion esul s (10)
15. End o
16. Fo each classi ica ion ou comes--
17. Compu e he e o a e ‘𝐸𝑅’ using (11)
18. Apply adap i e G adien me hod o
upda e weigh using (12)
19. End o
20. Ini ialize he popula ion o he weigh s
𝜗= 𝜗,𝜗,𝜗,….𝜗
21. Fo each weigh in popula ions
22. Compu e he i ness ‘𝐹’ using (14)
23. While (𝐼𝑡𝑒𝑟<𝐼𝑡𝑒𝑟)
24. Selec he cu en bes using
25. Gene a e new loca ion using (15)
26. Ve i y Seasonal Moni o ing Condi ion
using (16) (17)
27. 𝒊𝒇(𝑍𝑆 < 𝑍𝑆) hen
28. Reloca e he sea ch space
29.
𝐼𝑡𝑒𝑟
=
𝐼𝑡𝑒𝑟
+1
30. go o s ep 23
31. Else
32. Find he op imal weigh
33. End i
34. End while
35. Ob ain he inal classi ica ion esul s
using (18) (19) wi h so max ac i a ion
unc ion a ou pu laye
End
Algo i hm 1 ou lines he p ocess o
p edic ing di e en ypes o hy oid diseases wi h
minimal ime consump ion. Fo each inpu da a
sample, weigh s and biases a e assigned o he isible
laye o he deep belie ne wo k (DBN) a chi ec u e.
The inpu is hen ans e ed o he neu ons in he
hidden laye , whe e da a p ep ocessing is pe o med
o handle missing alues wi hin he da ase . Nex ,
signi ican ea u es a e selec ed in he subsequen
hidden laye . Classi ica ion is ca ied ou in hi d
hidden laye using he Sokal–Michene 's simple
ma ching me hod o compa e aining and es ing da a
samples. Based on his simila i y, di e en ypes o
hy oid diseases a e classi ied. A e classi ica ion, a
ine- uning p ocess is pe o med using he Squi el
Sea ch op imiza ion algo i hm. Ini ially, he numbe
o weigh s is de e mined, and a popula ion o
squi els ( ep esen ing he weigh s) is ini ialized
wi hin he sea ch space. The i ness o each squi el
is compu ed based on he classi ica ion e o . The
posi ion o each squi el is hen upda ed i e a i ely.
This p ocess con inues un il he maximum numbe o
i e a ions is eached. Th ough his i e a i e app oach,
he Squi el Sea ch algo i hm iden i ies he op imal
weigh alues ha minimize he classi ica ion e o .
Finally, he p edic ion ou comes a e de e mined by
minimizing he classi ica ion e o a he ou pu laye .
4. Expe imen al Se ings
In his sec ion, expe imen al e alua ion o he
p oposed, SSGODBNC and wo exis ing me hods
DSHM [1] and S acked Ensemble wi h IG ea u e
selec ion [2] a e implemen ed in Py hon high-le el
gene al-pu pose p og amming language. In o de o
conduc he expe imen , Thy oid disease da ase is
applied and i aken om he
h ps://www.kaggle.com/da ase s/emmanuel we / hy
oid-disease-da a. This s ep ensu es ha su icien
in o ma ion is a ailable o ain and alida e he model
Jou nal o Theo e ical and Applied In o ma ion Technology
31s May 2025. Vol.103. No.10
© Li le Lion Scien i ic
ISSN: 1992-8645 www.ja i .o g E-ISSN: 1817-3195
4283
2840 – 2855.
DOI: 10.1109/TAI.2023.3327981
[14] Mona Alnagga , Mohamed Handosa,
Tame Medha , M. Z. Rashad, “Thy oid
Disease Mul i-class Classi ica ion based
on Op imized G adien Boos ing Model”,
Egyp ian Jou nal o A i icial
In elligence, Volume 2, Issue 1, 2023,
Pages 1-
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