Indonesian
J
our
nal
of
Electrical
Engineering
and
Computer
Science
V
ol.
38,
No.
2,
May
2025,
pp.
1231
∼
1244
ISSN:
2502-4752,
DOI:
10.11591/ijeecs.v38.i2.pp1231-1244
❒
1231
T
extual
and
numerical
data
fusion
f
or
depr
ession
detection:
a
machine
lear
ning
framew
ork
Mohammad
T
ar
ek
Aziz
1
,
T
anjim
Mahmud
2
,
Md
F
aisal
Bin
Abdul
Aziz
3
,
Md
Ab
u
Bakar
Siddick
4
,
Md.
Maskat
Sharif
1
,
Mohammad
Shahadat
Hossain
5,6
,
Karl
Andersson
6
1
Department
of
Computer
Science
and
Engineering,
Chittagong
Uni
v
ersity
of
Engineering
and
T
echnology
,
Chittagong,
Bangladesh
2
Department
of
Computer
Science
and
Engineering,
Rang
amati
Science
and
T
echnology
Uni
v
ersity
,
Rang
amati,
Bangladesh
3
Department
of
Computer
Science
and
Engineering,
Comilla
Uni
v
ersity
,
Comilla,
Bangladesh
4
Department
of
Computer
Science
and
T
echnology
,
Beijing
Institute
of
T
echnology
,
Haidian,
China
5
Department
of
Computer
Science
and
Engineering,
Uni
v
ersity
of
Chittagong,
Chittagong,
Bangladesh
6
Cybersecurity
Laboratory
,
Lule
˚
a
Uni
v
ersity
of
T
echnology
,
Sk
ellefte
˚
a,
Sweden
Article
Inf
o
Article
history:
Recei
v
ed
Mar
28,
2024
Re
vised
Oct
13,
2024
Accepted
Oct
30,
2024
K
eyw
ords:
Chit-squared
test
CO
VID-19
Depression
Machine
learning
algorithms
Random
forest
ABSTRA
CT
Depression,
a
wi
despread
mood
disorder
,
signicantly
af
f
ects
global
mental
health.
T
o
mitig
ate
the
risk
of
recurrence,
early
detection
is
crucial.
This
study
e
xplores
socioeconomic
f
actors
contrib
uting
to
depression
and
proposes
a
no
v
el
machine
learning
(ML)-based
frame
w
ork
for
its
detection.
W
e
de
v
elop
a
tailored
questionnaire
to
collect
te
xtual
and
numerical
data,
follo
wed
by
rigorous
feature
selection
using
methods
lik
e
bac
kw
ard
remo
v
al
and
Pearson’
s
chi-squared
test.
A
v
ariety
of
ML
algorithms,
including
random
forest
(RF),
support
v
ector
ma-
chine
(SVM),
and
logistic
re
gression
(LR),
are
emplo
yed
to
create
a
predicti
v
e
classier
.
The
RF
model
achie
v
es
the
highest
accurac
y
of
96.85%,
highlighting
its
e
f
fecti
v
eness
in
identifying
depression
risk
f
actors.
This
research
adv
ances
depression
detection
by
inte
grating
socioeconomic
analysis
with
ML,
of
fering
a
rob
ust
tool
for
enhancing
predicti
v
e
accurac
y
and
enabling
proacti
v
e
mental
health
interv
entions.
This
is
an
open
access
article
under
the
CC
BY
-SA
license
.
Corresponding
A
uthors:
T
anjim
Mahmud
Rang
amati
Science
and
T
echnology
Uni
v
ersity
,
Rang
amati
4500,
Bangladesh
Email:
tanjim
cse@yahoo.com
1.
INTR
ODUCTION
The
rapid
adv
ancement
of
technology
and
human
skills
has
profoundly
inuenced
both
ph
ysical
and
mental
health
[1].
Despite
the
gro
wing
a
w
areness
of
ph
ysical
well-being,
mental
health
often
remains
under
-
v
alued,
leading
to
a
rise
in
mental
health
issues
that
e
v
olv
e
into
disorders,
including
depression.
Depression,
a
pre
v
alent
and
debilitating
mental
illness,
af
fects
indi
viduals
of
all
ages,
impairing
their
emotions,
thoughts,
and
beha
viors
[2].
In
the
2019–2020
academic
year
alone,
a
staggering
20.78%
of
adults
e
xperienced
mental
health
issues,
with
o
v
er
50
million
Americans
af
fected
[3].
The
global
CO
VID-19
pandemic
has
further
e
xacerbated
mental
health
challenges,
with
anxiety
and
hopelessness
increasing
by
25%
w
orldwide
[4].
Ev
en
post-reco
v
ery
from
the
virus,
indi
viduals
may
still
f
ace
lingering
ef
fects
contrib
uting
to
depression
[5].
The
pandemic’
s
multif
aceted
impact,
spanning
mental,
ph
ysical,
and
economic
realms,
has
manifes
ted
in
heightened
le
v
els
of
anxiety
,
insomnia,
subs
tance
ab
use,
and
other
adv
erse
outcomes.
Socioeconomic
f
actors
such
as
unemplo
yment,
f
amilial
disconnection,
and
social
isolation
ha
v
e
compounded
feelings
of
despair
and
disillusionment
[6].
Alarmingly
,
approximately
280
million
indi
viduals
w
orldwide
suf
fer
from
depression,
with
a
signicant
portion
recei
ving
inadequate
treatment,
particularly
in
lo
w-
and
middl
e-income
countries.
J
ournal
homepage:
http://ijeecs.iaescor
e
.com
Evaluation Warning : The document was created with Spire.PDF for Python.
1232
❒
ISSN:
2502-4752
The
global
suicide
rate,
a
tragic
consequence
of
untreated
depression,
underscores
the
ur
genc
y
of
addressing
mental
health
issues.
In
the
realm
of
depression
research,
se
v
eral
studies
ha
v
e
tackled
v
arious
aspects
of
mental
health
detection
using
machine
learning
(ML)
techniques.
Ferdo
wsy
et
al.
[7]
focused
on
predicting
obesity
risk,
emplo
ying
nine
ML
algorithms
on
a
dataset
comprising
1,100
instances
and
28
features.
Logistic
re
gression
(LR)
emer
ged
with
the
highest
accurac
y
of
97.09%,
while
gradient
boosting
(GB)
classier
yielded
the
lo
west
accurac
y
at
64.08%.
Feature
se
lection
tech-
niques
such
as
correlation
and
principal
component
analysis
(PCA)
were
utilized
to
identify
criti
cal
features.
Arif
et
al.
[8]
proposed
a
ML
technique
for
detecting
drug
addiction
in
the
population
of
Bangladesh.
Nine
ML
classiers
were
applied
to
a
dataset
containing
510
instances
and
23
attrib
utes,
with
LR
achie
ving
the
highest
accurac
y
of
97.91%.
Correlation
and
PCA
were
also
emplo
yed
for
feature
selection
to
enhance
model
performance.
Khatun
et
al.
[9]
in
v
estig
ated
the
detection
of
betel
nut
addiction
using
ML
te
chniques.
Among
six
classiers
applied
to
a
dataset
comprising
1001
samples
and
19
features,
random
forest
(RF)
achie
v
ed
the
highest
accurac
y
of
99.00%,
while
Nai
v
e
Bayes
(NB)
obtained
the
lo
west
accurac
y
at
91.04%.
Feature
selec-
tion
methods,
including
PCA
and
chi-square,
wer
e
emplo
yed
for
impro
v
ed
a
ccurac
y
.
Mia
et
al.
[10]
introduced
a
ML
approach
to
determine
the
re
gistration
status
of
students
in
pri
v
ate
uni
v
ersities
in
Bangladesh.
Se
v
en
ML
algorithms
were
applied
to
a
dataset
with
v
e
features,
achie
ving
an
accurac
y
of
85.76%
with
support
v
ector
machine
(SVM)
and
79.65%
with
RF
.
Shahriar
et
al.
[11]
aimed
to
predict
vulnerability
to
drug
addiction
using
ML
algorithms
.
Three
classiers
were
applied
to
a
dataset
comprising
498
samples
and
60
attrib
utes,
with
RF
achie
ving
the
highest
accurac
y
of
94.00%.
Lee
and
Kim
[12]
focused
on
predicti
ng
problematic
smartphone
use
using
ML
tech-
niques.
Among
three
classiers
applied
to
a
dataset
with
29,712
instances
and
27
features,
RF
achie
v
ed
the
highest
accurac
y
of
82.59%,
while
decision
tree
(DT)
had
the
lo
west
accurac
y
at
74.56%.
K
e
ya
et
al.
[13]
emplo
yed
ML
approaches
to
analyze
the
performance
of
g
arment
w
omen’
s
w
orking
status
in
Bangladesh.
Fi
v
e
ML
algorithms
were
applied
to
a
dataset
with
512
instances
and
13
features,
with
LR
achie
ving
the
highest
accurac
y
of
69%.
Go
vindasamy
and
P
alanichamy
[14]
utilized
ML
techniques
on
T
witter
data
to
detect
depres-
sion.
T
w
o
classiers
were
applied
to
datasets
with
1,000
and
3,000
instances
respecti
v
ely
,
with
both
models
achie
ving
an
accurac
y
of
around
92.34%
and
97.31%.
Sadeque
et
al.
[15]
e
xplored
depression
detection
in
so-
cial
media
by
focusing
on
indi
viduals
e
xhibiting
signs
of
depression
within
online
communities.
Amanat
et
al.
[16]
emplo
yed
deep
learning
algorithms
to
predict
depression,
achie
ving
an
accurac
y
of
99%
using
long
short-
term
memory
(LSTM)
and
recurrent
neural
netw
ork
(RNN)
algorithms
on
te
xtual
data.
Ag
ainst
this
backdrop,
our
research
endea
v
ors
to
contrib
ute
to
the
early
detection
of
depression
us-
ing
ML
algorithms
[17],
[18]
and
feature
selection
techniques
[19].
By
analyzing
a
dataset
comprising
6,186
instances
and
28
features
related
to
depression.
W
e
aim
to
identify
indi
viduals
at
risk
of
depression
and
distin-
guish
them
from
non-depressed
indi
viduals.
The
primary
objecti
v
es
of
our
study
include
detecting
depression
using
mix
ed
data
(both
numerical
and
t
e
xt
ual),
g
athering
comprehensi
v
e
data
through
tailored
questionnaires,
and
applying
feature
selection
techniques
to
enhance
model
accurac
y
.
The
main
contrib
utions
of
this
study
are
belo
w:
i)
De
v
elopment
of
a
combined
dataset
incorporating
both
te
xtual
and
numerical
data,
augmented
through
o
v
ersampling
and
chi-square
technique
to
increase
dataset
rob
ustness.
ii)
Utilization
of
the
de
v
eloped
dataset
to
train
ML
classiers
for
accurate
prediction
of
depression,
achie
v
ed
by
con
v
erting
dataset
into
binary
v
alues
for
labeling.
iii)
Proposal
of
a
RF
model,
demonstrating
s
uperior
performance
with
training
accurac
y
of
97.37%
and
testing
accurac
y
of
96.85%,
surpassing
other
classiers.
i
v)
Application
of
nine
ML
classiers
to
ef
fecti
v
ely
detect
depression
from
mix
ed
data,
achie
ving
accurac
y
rates
e
xceeding
78%
in
most
cases.
In
this
study
,
the
proposed
system
architecture
in
section
2,
the
result
analysis
in
section
3,
and
nally
the
conclusion
and
future
plan
in
section
4
are
discussed
sequentially
.
2.
METHOD
The
w
orking
procedure
of
the
proposed
system
architecture
is
illustrated
in
Figure
1,
and
an
e
xplana-
tion
of
the
mechanism
is
described
in
belo
w
.
Indonesian
J
Elec
Eng
&
Comp
Sci,
V
ol.
38,
No.
2,
May
2025:
1231–1244
Evaluation Warning : The document was created with Spire.PDF for Python.
Indonesian
J
Elec
Eng
&
Comp
Sci
ISSN:
2502-4752
❒
1233
Figure
1.
Proposed
system
architecture
of
depression
detection
2.1.
Dataset
Data
collection
w
as
conducted
through
online
sources
including
websites,
web
portals,
and
soci
al
media
platforms
such
as
F
acebook,
Instagram,
and
T
witter
,
utilizing
the
data-collecting
tool
F
acepager
.
Addi-
tionally
,
f
ace-to-f
ace
intervie
ws
were
conducted
with
indi
viduals
af
liated
with
medical
institutions,
hospitals,
and
clinics.
This
approach
in
v
olv
ed
interacting
with
indi
viduals
who
were
diagnosed
with
depression
or
e
x-
hibited
symptoms
of
mental
disorder
,
al
beit
being
comparati
v
ely
challenging
and
time-consuming.
Despite
the
challenges,
a
total
of
520
records
were
obtained
through
these
tw
o
methods
[20].
T
o
comprehend
the
re
gional
f
actors
contrib
uting
to
depression,
a
questionnaire
w
as
de
v
eloped
by
re
vie
wing
literature,
rele
v
ant
websites,
and
consulting
re
gional
psychiatrists.
Initially
,
a
draft
quest
ionnaire
w
as
created,
which
w
as
later
rened
to
in-
clude
a
total
of
28
items.
These
questions
are
structured
as
multiple
choice
questions
(MCQs)
with
tw
o
or
more
possible
solutions.
A
dataset
comprising
520
cases
w
as
collected
based
on
the
responses
to
these
questions,
which
serv
e
as
input
data
for
the
proposed
system.
Each
question
in
the
questionnaire
presents
respondents
with
specic
answer
options,
such
as
binary
choices
lik
e
“yes”
or
“no”.
The
data
collection
process
is
depicted
in
Figure
2,
encompassing
v
arious
steps
outlined
belo
w
.
D
a
t
a
C
o
l
l
e
ct
i
o
n
Me
t
h
o
d
s
On
l
i
n
e
S
u
r
v
e
y
Qu
e
s
t
i
on
n
a
i
r
e
An
a
l
y
s
i
s
R
a
w
D
a
t
a
se
t
Da
ta
P
r
e
p
r
o
ce
ssi
n
g
Da
ta
A
u
g
me
n
ta
tio
n
F
e
a
t
u
r
e
A
n
a
l
ysi
s
u
si
n
g
C
h
i
-
sq
u
a
r
e
d
T
e
st
E
l
i
m
i
n
a
t
e
d
L
e
ss
N
e
ce
ssa
r
y
Fe
a
tu
re
s
Figure
2.
W
orking
process
of
data
collection
and
feature
analysis
with
chi-squared
test
2.1.1.
Questionnair
e
analysis
W
e
adminis
tered
a
set
of
28
questions
to
users
to
g
ather
data,
with
the
intention
of
e
xtracting
i
nforma-
tion
for
future
analysis
based
on
their
responses
[21].
These
questions,
detailed
in
T
able
1,
encompass
v
arious
aspects
related
to
the
indi
vidual’
s
circumstances,
including
inquiries
about
age,
f
amily
crises,
and
mental
pres-
sure
[22].
The
solutions
to
these
questions
typically
in
v
olv
e
more
than
tw
o
options,
such
as
“yes”
or
“no”,
with
each
response
contrib
uting
to
the
assessment
of
the
indi
vidual’
s
lik
elihood
of
e
xperiencing
depression.
F
or
T
e
xtual
and
numerical
data
fusion
for
depr
ession
detection:
...
(Mohammad
T
ar
ek
Aziz)
Evaluation Warning : The document was created with Spire.PDF for Python.
1234
❒
ISSN:
2502-4752
instance,
questions
pertaining
to
age,
f
amily
dynamics,
and
mental
well-being
are
particularly
rele
v
ant
indica-
tors.
F
ollo
wing
the
collection
of
responses,
the
model
can
then
determine
whether
the
indi
vidual
e
xhibits
signs
of
depression
or
not.
F
or
a
comprehensi
v
e
list
of
questions,
please
refer
to
T
able
1,
while
T
able
2
presents
an
analysis
of
the
corresponding
solutions.
T
able
1.
Questions
to
collect
data
from
person
intervie
w
or
website
making
a
dataset
S.L
User’
s
information
to
nd
out
depression
1
Y
our
Age
range?
2
Y
our
gender?
3
Y
our
occupation?
4
What
kind
of
relationship
is
between
you
and
your
f
amily?
5
Can
you
share
your
personal
matter
with
your
f
amily
members
easily?
6
Do
you
ha
v
e
a
f
amily
crisis?
7
Is
your
f
amily
supporti
v
e?
8
Do
you
ha
v
e
f
aced
an
y
V
iolence
in
your
f
amily?
9
Ho
w
did
you
spend
your
lockdo
wn
time
during
CO
VID
19?
10
During
CO
VID
19
lockdo
wn,
did
you
feel
lonely?
11
If
you
are
a
student,
are
you
satised
with
your
academic
result?
12
If
you
are
a
student,
Online
class
during
CO
VID
19
af
fect
your
mental
health?
13
Did
you
retreat
from
your
study
because
of
CO
VID
19?
14
If
you
are
an
under
graduate,
ha
v
e
you
f
allen
in
Session
clutter
because
of
CO
VID
19?
15
Are
you
afraid
to
apply
abroad
for
your
higher
study
because
of
CO
VID
19?
16
What
kind
of
relationship
is
between
you
and
your
friends/
colleagues?
17
What
is
your
relationship
status?
18
Are
you
happ
y
with
your
partner?
19
Are
you
tensed
about
an
ything
that
you
cannot
for
get
an
ymore?
20
Are
you
w
orried
about
the
uncertainty
of
getting
a
job
because
of
CO
VID
19?
21
Are
you
happ
y
with
your
current
situation?
22
Did
you
attack
yourself
for
some
reason
during
CO
VID
19?
23
Did
you
loss
your
closer
an
yone
due
to
CO
VID
19
and
you
cannot
for
get
this
situation
till
no
w?
24
Which
things
are
al
w
ays
pressurized
on
you?
25
Are
you
addicted
to
an
y
drugs?
26
Ha
v
e
you
e
v
er
e
xperie
nced
b
ullying
from
friends
or
through
social
media?
27
Do
you
spend
most
of
your
time
on
Social
media?
28
Do
you
ha
v
e
s
leeping
(insomnia)
problem?
T
able
2.
T
op
10
features
of
collected
ra
w
dataset
with
top
v
e
v
alue
Features
Data
1
Data
2
Data
3
Data
4
Data
5
Age
18-25
18-25
25-40
18-25
18-25
Gender
Female
Female
Male
Male
Male
Occupation
St
udent
Student
Service
holder
Student
Student
Relationships
Strong
Strong
Normal
Strong
Strong
Data
sharing
No
Y
es
Y
es
Y
es
Y
es
F
amily-crisis
No
No
No
No
No
Mental
pressure
Y
es
No
Y
es
No
No
Supporti
v
e-f
amily
Y
es
Y
es
Y
es
Y
es
Y
es
F
amily-violence
No
No
No
No
Y
es
V
iolence
pressure
No
Y
es
No
No
Y
es
2.1.2.
Raw
dataset
After
nishing
the
data
collection,
we
got
a
total
of
520
records
with
28
features.
The
features
are:
‘
Age’,
‘gender’,
‘occupation’,
‘relation-with-f
amily’,
‘shared-personal-matter
-with-f
amily’,
‘f
amily-crisis’,
‘mental-pressure-due-to-crisis’,
‘supporti
v
e-f
amily’,
‘f
aced-V
iolence’,
‘mental-pressure-due-to-violence’,
‘spend-lockdo
wn’,
‘feel-lonely-during-CO
VID19’,
‘Satised-academic-result’,
‘mental-pressure-due-to-
online-class’,
‘retreat-from-study-due-to-CO
VID19’,
‘mental-pressure-due-to-retreat’,
‘f
allen-into-session-
clutter’,
‘mental-pressur
e-due-to-session-clutter’,
‘afraid-apply-abroad’,
‘relati
on-with-friends’,
‘relationship-
status’,
‘happ
y-with-partner’,
‘tensed-something’,
‘w
orried-uncertainty-getting-job-CO
VID-19’,
‘mental-
pressure-due-to-getting-job’,
‘happ
y-with-current-situation’,
‘attack-yourself-in-CO
VID-19-situation’,
‘lost-
Indonesian
J
Elec
Eng
&
Comp
Sci,
V
ol.
38,
No.
2,
May
2025:
1231–1244
Evaluation Warning : The document was created with Spire.PDF for Python.
Indonesian
J
Elec
Eng
&
Comp
Sci
ISSN:
2502-4752
❒
1235
closer
-during-CO
VID-19’,
‘pressurized-on-you’,
‘addicted’,
‘b
ullied-social
media’,
‘mental-pressure-due-to-
b
ullied’,
‘spend-much-time-social-media’,
‘insomnia-problem’.
Only
“age”
contains
the
numerical
v
alue,
and
the
other
features
contain
the
te
xtual
v
alue.
In
most
cases,
te
xtual-based
features
ha
v
e
tw
o
v
alues:
yes
or
no.
The
top
10
features
are
sho
wn
in
T
able
2
with
their
v
alues.
The
other
18
features
also
ha
v
e
almost
the
same
v
alue,
lik
e
data
sharing
and
violent
pressure.
That
means
the
y
contain
tw
o
Boolean
v
alues:
yes
and
no.
So,
we
ignored
adding
these
features
here
due
to
length.
2.2.
Data
labeling
Data
labeling,
also
kno
wn
as
data
annotation,
plays
a
pi
v
otal
role
in
ML
tasks
[23].
Labeled
data
is
ess
ential
to
pre
v
ent
issues
such
as
o
v
ertting
or
undertting,
which
may
occur
when
using
unlabel
ed
data.
W
ithout
proper
labeling,
classiers
may
misclassify
or
e
xhibit
reduced
accurac
y
,
thereby
disrupting
the
desired
outcome
[24].
In
our
proposed
method,
we
opted
for
binary
transformation
as
mos
t
features
possess
tw
o
distinct
v
al-
ues,
such
as
“yes”
or
“no”.
T
able
3
displays
the
binary-transformed
v
alues
for
a
total
of
28
features,
sho
wcasing
the
top
5
v
alues.
While
presenting
the
top
10
features
in
this
table,
we
omitted
the
remaining
features
as
the
y
e
xhibit
similar
characteristics.
T
able
3.
Binary
transformed
v
alue
for
some
features
with
top
v
e
v
alues
Features
Data
1
Data
2
Data
3
Data
4
Data
5
Relationships
1
1
0
1
1
Data
sharing
0
1
1
1
1
F
amily-crisis
0
0
0
0
0
Mental
pressure
1
0
1
0
0
Supporti
v
e-f
amily
1
1
1
1
1
F
amily-violence
0
0
0
0
1
V
iolence
pressure
0
0
0
0
1
2.3.
Data
pr
epr
ocessing
The
questionnaire
serv
es
the
purpose
of
g
athering
essential
insights
into
the
primary
causes
of
de
p
r
es-
sion
pre
v
alent
in
Bangladesh.
Data
coll
ection
w
as
conducted
through
tw
o
methods:
online
search
and
personal
intervie
ws.
Ultimately
,
a
total
of
520
instances
were
amassed.
The
subsequent
step
in
our
methodology
in
v
olv
es
data
preprocessing,
which
encompasses
four
fundamental
tasks
[25],
[26]:
1.
Null
v
alues
were
lled
electronically
to
ensure
completeness
of
the
dataset.
2.
Missing
v
alues
labeled
as
“nan”
were
manually
addressed
to
ensure
compatibility
with
subsequent
oper
-
ations.
3.
Some
feature
v
alues
were
updated
to
binary
“yes”
or
“no”
format
to
align
with
the
classication
method
to
be
used
in
the
model.
4.
Redundant
v
alues
were
eliminated
from
the
dataset
to
streamline
the
analysis
process.
2.4.
Data
augmentation
Gi
v
en
the
imbalanced
nature
of
our
dataset,
we
emplo
yed
o
v
ersampling
techniques
[27],
[28]
to
rec-
tify
the
imbalance.
A
commonly
used
method
in
the
literature
for
generating
additi
on
a
l
samples
is
the
synthetic
minority
o
v
er
-sampling
technique
(SMO
TE).
Through
o
v
ersampling,
our
dataset
e
xpanded
by
approximately
6,186
samples,
resulting
in
a
well-balanced
dataset
suitable
for
training
our
ML
model.
Specically
,
T
able
4
sho
wcases
a
selection
of
se
v
en
randomly
chosen
features
along
with
their
corres
po
ndi
ng
matrix
format,
fol-
lo
wing
o
v
ersampling.
Other
features
were
disre
g
arded
due
to
their
similarity
.
T
able
4.
Ov
er
sampled
matrix
format
of
the
features
Feature
Matrix
format
Gender
(610,
26)
shared-personal
Matter
-with-f
amily
(702,
26)
Supporti
v
e-f
amily
(1100,
26)
Mental
Pressure-due-to-violence
(1592,
26)
Addicted
(5846,
26)
Insomnia-problem
(6258,
26)
Attack-yourself-in-CO
VID19
situation
(2948,
26)
T
e
xtual
and
numerical
data
fusion
for
depr
ession
detection:
...
(Mohammad
T
ar
ek
Aziz)
Evaluation Warning : The document was created with Spire.PDF for Python.
1236
❒
ISSN:
2502-4752
2.5.
Chi-squar
ed
test
analysis
and
featur
e
selection
W
ithout
assuming
an
y
particular
distrib
ution,
the
Pearson’
s
chi-squared
test
of
independence
is
a
sta-
tistical
technique
used
to
e
v
aluate
the
relationship
between
tw
o
cate
gorical
v
ariables
based
on
their
frequencies
[29].
This
test
enables
us
to
determine
whether
there
is
a
signicant
correlation
between
the
predictor
v
ariables
and
the
tar
get
v
ariable
in
our
dataset,
where
all
of
the
v
ariables
are
cate
gorical.
This
test’
s
null
h
ypothesis
presupposes
that
there
is
no
relationship
between
the
v
ariables.
The
chi-squared
test,
sho
wn
in
Figure
3,
can
be
used
to
ascertain
whether
the
tar
get
v
ariable
and
the
feature
v
ariables
are
dependent
on
one
another
.
This
is
accomplished
by
creating
a
contingenc
y
table,
which
arranges
the
v
ariable
frequencies
in
an
or
g
anized
manner
and
is
also
referred
to
as
a
cross-tab
ulation
or
tw
o-
w
ay
table.
Based
on
the
v
ariations
between
observ
ed
and
e
xpected
frequencies
in
the
contingenc
y
table,
the
test
computes
a
chi-squared
statistic
[30].
W
e
reject
the
null
h
ypothesis
and
conclude
that
there
is
a
signicant
association
between
the
v
ariables
if
the
computed
chi-squared
statistic
is
greater
than
a
critical
v
alue
determined
by
the
de
grees
of
freedom
and
selected
signicance
le
v
el.
In
contrast,
if
the
computed
statistic
is
less
than
the
critical
v
alue,
we
are
unable
to
reject
the
null
h
ypothesis,
indicating
that
there
is
no
meaningful
correlation.
The
analytical
form
of
the
chi-squared
test
statistic
for
a
contingenc
y
table
with
r
ro
ws
and
c
columns
is
gi
v
en
by:
χ
2
=
r
X
i
=1
c
X
j
=1
(
O
ij
−
E
ij
)
2
E
ij
where:
−
O
ij
represents
the
observ
ed
frequenc
y
in
the
i
th
ro
w
and
j
th
column
of
the
contingenc
y
table.
−
E
ij
represents
the
e
xpected
frequenc
y
in
the
i
th
ro
w
and
j
th
column,
calculated
under
the
assumption
of
independence
between
the
tw
o
cate
gorical
v
ariables.
The
chi-squared
test
statistic
follo
ws
a
chi-squared
distri
b
ut
ion
with
de
grees
of
freedom
equal
to
(
r
−
1)(
c
−
1)
under
the
null
h
ypothesis
of
independence
between
the
v
ariables.
By
comparing
the
calcu-
lated
chi-squared
statistic
with
the
critical
v
alue
from
the
chi-squared
distrib
ution,
one
can
determine
whether
to
reject
or
f
ail
to
reject
the
null
h
ypothesis.
Using
the
chi-square
test,
based
on
the
relationship
between
each
independent
feature
and
tar
get
v
ariable,
we
selected
25
v
ariables.
F
or
one
independent
v
ariable,
the
null
h
y-
pothesis
w
as
f
alse.
So
we
remo
v
ed
this
feature.
Among
the
original
26
features
in
our
data
collection,
the
25
special
v
ariables
used
for
creation
ha
v
e
been
e
v
aluated
as
ha
ving
the
highest
impact.
Figure
3.
Feature
analysis
with
chi-test
for
tar
get
and
other
v
ariables
2.6.
Classiers
In
this
study
,
we
applied
a
total
of
9
ML
classiers
to
detect
depression
from
both
te
xtual
and
numerical
datasets.
The
classiers
are
used
to
nd
out
the
depression
with
optimum
solutions.
The
used
classiers
are
described
belo
w
.
2.6.1.
Random
f
or
est
RF
is
a
popular
ensemble
learning
algorithm
that
constructs
multiple
DTs
during
training
and
com-
bines
their
predictions
to
reduce
o
v
ertting
and
impro
v
e
accurac
y
for
both
re
gression
and
classication
tasks
[31],
[32].
Figure
4
sho
ws
w
orking
process
of
the
proposed
RF
classiers.
Indonesian
J
Elec
Eng
&
Comp
Sci,
V
ol.
38,
No.
2,
May
2025:
1231–1244
Evaluation Warning : The document was created with Spire.PDF for Python.
Indonesian
J
Elec
Eng
&
Comp
Sci
ISSN:
2502-4752
❒
1237
Figure
4.
W
orking
process
of
the
proposed
RF
classiers
2.6.2.
Multilay
er
per
ceptr
on
The
multilayer
perceptron
(MLP)
is
a
basic
articial
neural
netw
ork
(ANN)
with
a
layered
archite
cture
consisting
of
an
input
layer
,
one
or
more
hidden
layers,
and
an
output
layer
,
enabling
it
to
model
comple
x
non-
linear
relationships
between
inputs
and
outputs.
2.6.3.
Decision
tr
ee
The
DT
algorithm
is
a
popular
and
straightforw
ard
ML
method
that
constructs
a
tree-lik
e
s
tructure
for
re
gression
and
classication
tasks,
of
fering
ease
of
use,
interpretability
,
and
the
ability
to
handle
both
cate
gorical
and
numerical
data.
2.6.4.
Logistic
r
egr
ession
LR
is
a
statistical
technique
used
for
binary
classication
problems,
modeling
the
probability
of
a
binary
outcome
based
on
predictor
v
ariables
and
con
v
erting
ra
w
predictions
into
probabilities
using
the
logistic
function
[33].
2.6.5.
Gradient
boosting
GB
is
an
ensemble
learning
technique
that
b
uilds
a
strong
predicti
v
e
model
by
iterati
v
ely
adding
weak
learners,
typically
DTs,
each
impro
ving
on
the
residuals
of
the
pre
vious
models
to
enhance
o
v
erall
accurac
y
.
2.6.6.
Gaussian
Nai
v
e
Bay
es
Gaussian
NB
is
a
probabili
stic
classication
algorithm
based
on
Bayes’
theorem,
assuming
feat
ures
are
independent
and
normall
y
distrib
uted,
making
it
ef
fecti
v
e
for
tasks
lik
e
te
xt
classication
and
spam
ltering,
especially
with
continuous
data.
2.6.7.
Support
v
ector
machine
SVM
is
a
po
werful
supervised
learning
algorithm
that
determines
the
optimal
h
yperplane
to
sepa-
rate
classes
in
the
feature
space,
useful
for
both
linear
and
non-linear
data,
particularl
y
in
high-dimensional
spaces
[34].
2.6.8.
K-near
est
neighbors
K-nearest
neighbors
(KNN)
i
s
a
non-parametric
ML
algorithm
that
assigns
a
data
point
to
the
m
ajority
class
among
its
nearest
neighbors,
commonly
used
for
tasks
with
non-linear
decision
boundaries
[35].
T
e
xtual
and
numerical
data
fusion
for
depr
ession
detection:
...
(Mohammad
T
ar
ek
Aziz)
Evaluation Warning : The document was created with Spire.PDF for Python.
1238
❒
ISSN:
2502-4752
2.6.9.
AdaBoost
AdaBoost
(AD
A-B),
or
adapti
v
e
boosting,
is
an
ensemble
technique
that
combines
se
v
eral
weak
learn-
ers
by
adjusting
their
weights
based
on
misclassied
instances,
enhancing
classication
performance
through
iterati
v
e
impro
v
ement.
3.
RESUL
TS
AND
DISCUSSION
3.1.
Data
splitting
The
dataset
underwent
a
di
vision
into
training
and
testing
subsets,
allocated
at
an
80%
to
20%
ratio,
respecti
v
ely
.
Both
subsets
utilized
binary
e
xpressed
v
alues
for
training
and
testing
purposes.
V
arious
ML
techniques
were
emplo
yed
to
construct
a
predicti
v
e
binary
cla
ssier
using
the
training
set.
These
techniques
included
GB,
AD
A-B
classier
,
KNN,
g
aussian
NB,
SVM,
DT
,
RF
,
and
LR.
The
selected
tar
get
v
ariable
for
the
classier
w
as
“F
amily
crisis,
”
chosen
due
to
the
signicant
role
of
nancial
issues
in
male
depression.
The
model’
s
output
w
as
compared
ag
ainst
the
tar
get
v
ariable
of
the
test
set
to
e
v
aluate
the
accurac
y
and
ef
fecti
v
eness
of
the
classier
.
3.2.
Used
classier
parameters
W
e
re
vie
w
the
man
y
parameters
utilized
to
b
uild
the
classiers
and
run
e
xperiments
in
this
part.
The
parameters
v
ary
based
on
the
classiers.
See
the
parameter
details
in
T
able
5
that
are
used
in
the
study
.
T
able
5.
Classier
parameter
details
Classiers
P
arameter
LR
Dual=F
alse,
solv
er=‘lbfgs’,
penalty=‘l2’
DT
threshold=‘gini’,
maximum
depth=25,
random
state=0
splitter
=
“optimal”
RF
The
smallest
samples
are
“4
for
departs
9
for
splits
in
half,
114
for
estimations,
and
22
for
random
states,
”
while
the
maximum
depth
is
25
and
the
maximum
features
are
“log2.
”
SVM
random
state=0,
k
ernel=‘rbf
’
KNN
weights=‘uniform’
Gaussian
NB
v
arsmoothing=1e-9
MLP
Random
state
=
0,
a
cti
v
ation
=
logistic,
solv
er
=
lbfgs
GB
classiers
Loss
=
“de
viance,
”
learning
rate
=
0.1,
and
n
estimators
=
100
AD
A-B
classier
n
estimators=
50
3.3.
Ev
aluation
metric
In
this
study
,
we
used
classiers
to
predict
sad
indi
vidual
s.
W
e
used
the
Scikit-learn
function
accurac
y
score()
[36],
[37],
which
uses
the
test
dataset’
s
classiers’
projected
results,
to
determine
the
accurac
y
.
TP
(true
positi
v
e):
it
indicates
the
amount
of
accurately
predicted
dataset
by
the
classier
.
FP
(f
alse
positi
v
e):
the
amount
of
dataset
had
the
classier
incorrectly
predicted
as
being
in
good
health
when
the
y
are
depressed.
FN
(f
alse
ne
g
ati
v
e):
the
classier
mislabeled
a
number
of
outcomes
as
ha
ving
addictions
when,
in
f
act,
the
y
were
health
y
in
the
sample.
TN
(true
ne
g
ati
v
e):
the
number
of
outcomes
in
the
dataset
is
correctly
classied
as
depressed
by
the
classier
.
Accur
acy
=
T
P
+
T
N
T
P
+
T
N
+
F
P
+
F
N
(1)
P
r
ecision
=
T
P
T
P
+
F
P
(2)
R
ecal
l
=
T
P
T
P
+
F
N
(3)
F
1
=
2
∗
P
r
ecision
∗
R
ecal
l
P
r
ecision
+
R
ecal
l
=
2
∗
T
P
2
∗
T
P
+
F
P
+
F
N
(4)
In
addition
to
guring
out
these
numbers,
we
also
measured
the
A
UC
v
alue
and
produced
the
R
OC
curv
e
(recei
v
er
operator
characteristic).
In
a
R
OC
diagram,
the
X-
and
Y
-ax
es
for
the
TP
rate
(recall)
and
Indonesian
J
Elec
Eng
&
Comp
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V
ol.
38,
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2,
May
2025:
1231–1244
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Sci
ISSN:
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❒
1239
FP
rate
(1-TNR)
are
displayed,
respecti
v
ely
.
Comparing
the
potenc
y
of
dif
ferent
prediction
models
is
done
using
the
tw
o-dimensional
area
under
the
R
OC
curv
e,
or
A
UC.
A
higher
A
UC
v
alue
typically
indicates
bet-
ter
prediction
performance.
The
classier
that
performs
t
he
best
is
chosen
after
taking
i
nto
account
all
of
these
v
ariables.
3.4.
Confusion
matrix
analysis
If
the
e
xplanation
of
error
analysis
were
used,
it
w
ould
be
simpler
to
comprehend
ho
w
well
the
chosen
classier
performed.
A
confusion
matrix
mak
es
error
analysis
easier
to
understand.
The
v
alue
of
it,
the
best
classier
after
training
with
28
features
is
displayed
in
Figure
5.
Out
of
the
1,238
total
predictions,
848
re-
spondents
were
correctly
recognized
as
being
in
good
health,
while
17
health
y
respondents
were
misclassied
as
being
in
bad
health.
In
the
instance
of
depression
predictions,
41
depressed
respondents
were
mistak
enly
classed
as
health
y
,
while
the
remaining
346
respondents
were
accurately
recognized.
Compared
to
depression
responses,
the
classier
mak
es
fe
wer
mistak
es
in
the
health
y
class.
Figure
5.
Confusion
matrix
of
RF
classier
3.5.
Accuracy
scor
es
Since
the
feature
analysis
is
performed
using
the
chi-squar
ed
test,
and
we
selected
28
features
based
on
it,
we
performed
nine
algorithms
sho
wn
in
T
able
6
and
analyzed
their
accurac
y
score.
W
e
got
the
highest
accurac
y
in
DT
classiers
at
99.49%
for
training
and
96.44%
for
testing
accurac
y
.
3.6.
R
OC
and
A
UC
v
alue
analysis
A
classier
,
named
RF
,
has
the
best
accurac
y
among
trained
classiers,
according
to
an
analysis
of
the
data
in
T
able
6.
W
e
need
to
look
at
the
R
OC
signal
for
e
v
ery
classier
to
test
performance
in
terms
of
sensiti
vity
and
specicity
in
addition
to
classication
accurac
y
.
Comparing
classiers
w
as
done
using
A
UC
v
alues
and
R
OC
curv
es,
which
pro
vide
reliable
descriptions
of
discriminating
skills.
Accurac
y
ratings
may
not
fully
e
xplain
the
situation,
thus
A
UC
v
alues
are
necessary
to
comprehend
ho
w
the
classiers
performed
in
both
the
health
y
and
addicted
classes
in
our
study
.
By
comparing
accurac
y
scores
and
A
UC
v
alues,
we
may
arri
v
e
at
a
con
vincing
decision
on
which
model
is
best.
The
results
presented
in
T
able
6
and
Figure
6
sho
w
that
the
RF
classier
has
the
highest
correlation
when
we
look
at
the
A
UC
result
for
the
learners
who
ha
v
e
the
highest
accurac
y
scores,
with
a
success
rate
of
96.85%
and
a
mean
A
UC
of
0.99.
Figure
7
sho
ws
R
OC
curv
es
for
SVM,
KNN,
and
AD
A-B
classiers.
T
able
6.
T
raining
and
testing
accurac
y
of
the
used
classiers
Classier
T
raining
accurac
y
%
T
esting
accurac
y
%
LR
85.79
87.80
DT
99.49
96.44
RF
97.37
96.85
SVM
95.98
95.64
KNN
97.33
96.20
Gaussian
NB
78.48
79.89
MLP
99.49
96.61
GB
classiers
92.92
93.38
AD
A-B
classier
86.50
88.61
T
e
xtual
and
numerical
data
fusion
for
depr
ession
detection:
...
(Mohammad
T
ar
ek
Aziz)
Evaluation Warning : The document was created with Spire.PDF for Python.
1240
❒
ISSN:
2502-4752
Figure
6.
R
OC
curv
es
for
MLP
,
RF
,
DT
,
L
T
,
GB,
and
g
aussian
NB
classiers
Figure
7.
R
OC
curv
es
for
SVM,
KNN,
and
AD
A-B
classiers
3.7.
Pr
ecision,
r
ecall,
and
F1-scor
e
analysis
of
the
used
classiers
The
classier
with
the
best
accurac
y
and
A
UC
v
alue
among
all
of
them
is
a
RF
classier
trained
with
28
feature
v
ariables.
Consider
T
able
7,
which
contains
e
xplanations
of
each
of
the
classiers
mentioned
abo
v
e
along
with
additional
metrics
lik
e
recall
and
precision
[38],
[39].
T
able
7.
Precision,
recall,
and
F1-score
v
alue
for
all
classier
Classier
Class
Precision
Recall
F1-score
LR
Health
y
0.90
0.92
0.91
Depressed
0.82
0.78
0.80
DT
Health
y
0.97
0.97
0.97
Depressed
0.94
0.94
0.94
RF
Health
y
0.97
0.99
0.98
Depressed
0.97
0.92
0.95
SVM
Health
y
0.96
0.98
0.97
Depressed
0.96
0.90
0.93
KNN
He
alth
y
0.97
0.98
0.97
Depressed
0.94
0.93
0.94
Gaussian
NB
Health
y
0.91
0.79
0.84
Depressed
0.63
0.82
0.72
MLP
Health
y
0.97
0.98
0.98
Depressed
0.95
0.94
0.94
GB
Health
y
0.94
0.97
0.95
Depressed
0.93
0.85
0.89
AD
A-B
Health
y
0.90
0.94
0.92
Depressed
0.85
0.76
0.80
Indonesian
J
Elec
Eng
&
Comp
Sci,
V
ol.
38,
No.
2,
May
2025:
1231–1244
Evaluation Warning : The document was created with Spire.PDF for Python.