Improving the Instance Selection Method for Better Detection of
Depression in Children and Adolescents
Ariane C. B. da Silva
1 a
, Maycoln L. M. Teodoro
2 b
and Cristiane N. Nobre
1 c
Institute of Exact Sciences and Informatics, Pontifical Catholic University of Minas Gerais,
Dom Jos
e Gaspar, Belo Horizonte, Brazil
Department of Psychology, Federal University of Minas Gerais, Belo Horizonte, Brazil
Depression, Adolescence, Children, Machine Learning, Instance Selection.
Depression is the leading global cause of disability and often begins in adolescence, a critical period for de-
veloping depressive symptoms. Major depressive disorder in the early stages of life is common worldwide
but challenging to diagnose. Identifying the most striking profiles of depression in children and adolescents
could benefit the training and performance of Machine Learning models and thus help in the diagnosis. In-
stance Selection is one of the most applied methods for data reduction, allowing the most significant samples
to represent them. This work seeks to improve the SI with the Ant Colony Optimization heuristic, introducing
stochasticity control to better characterize profiles of children and adolescents with depression. The proposed
technique increased the detection rate of individuals with high symptoms in all evaluated algorithms between
0.07 and 8.93 percentage points.
Depression is a leading cause of disability around
the world and contributes significantly to the global
burden of disease. The World Health Organization
(WHO, 2022) estimates that over 300 million people
live with depression worldwide. It is the most sig-
nificant contributor to deaths by suicide (almost 800
thousand per year) and the most critical contributor to
global disability (7.5% of all years lived with disabil-
ity). Despite being common worldwide, the diagnosis
of depression in adolescence is still challenging since
it presents a wide range of symptoms that can be con-
fused with the natural alterations pertinent to this pe-
riod of life. In addition, Johnson et al. (2018) relate
depression in adulthood to its onset in the early stages
of life and emphasize the importance of identifying it
and starting treatment as soon as possible.
In Machine Learning (ML), the performance of
classification algorithms depends on the training
data’s quality. Thus, removing noise, outliers, and
other instances from the training set that could be
harmful or misleading for the algorithm that learns
a model is crucial. One widely applied method is In-
stance Selection (IS), whose main objective is to se-
lect the most significant instances of the original base.
The IS issue represents a combinatorial optimization
task that several heuristics can solve (Salama et al.,
2016). This work used the Ant Colony Optimization
(ACO) heuristic (Dorigo et al., 2006) due to its char-
acteristics of being able to be applied to different dis-
crete optimization problems with relatively few mod-
ifications (essential to generalize the possibilities of
use in different contexts of the library that we made
available), can be used in dynamic applications, is lit-
tle affected by the initialization condition and is less
likely to get stuck in local optima than conventional
greedy algorithms (Salama et al., 2016).
Concerning research involving Data Reduction
with IS and ACO, much scientific effort has been
employed to look for a reduced set of instances to
mitigate the low computational efficiency and high
storage requirements (Salama et al., 2016), (Miloud-
Aouidate and Baba-Ali, 2013), (Akinyelu, 2020),
(Gong et al., 2021), (El Bakrawy et al., 2022), (Hott.
et al., 2022). However, in the context of health, IS
with ACO can be used with a more specific objective.
According to Salama et al. (2016), IS is beneficial to
reduce the training time and improve the characteri-
zation of the instances, which would be of great value
B. da Silva, A., Teodoro, M. and Nobre, C.
Improving the Instance Selection Method for Better Detection of Depression in Children and Adolescents.
DOI: 10.5220/0012355600003657
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2024) - Volume 2, pages 404-411
ISBN: 978-989-758-688-0; ISSN: 2184-4305
Proceedings Copyright © 2024 by SCITEPRESS Science and Technology Publications, Lda.
in applications with health-oriented databases, such
as the investigation of depression, providing a more
representative model. Unlike the typical approach
in works related to IS, in this paper, the selected in-
stances were evaluated for the performance achieved
in the classifiers and qualitatively to characterize their
depression profiles better.
Thus, this study aims to apply the IS with ACO
to obtain more efficient classification models to iden-
tify depression in children and adolescents and seek a
better characterization of their profiles. Besides, we
introduced to the algorithm a new parameter to con-
trol the probability of instance selection, allowing us
to adjust the algorithm for a more or less exploratory
search in the sample space. Finally, a Python li-
brary package was available
for using the algorithm
in other contexts.
2.1 Instance Selection with ACO
The Instance Selection (IS) technique requires a com-
plete search of all possible combinations of instances
to find the best set. This study used the ACO heuristic
(Dorigo et al., 2006) to find the appropriate subset of
data that best characterizes the original set based on
the accuracy obtained by the classifier kNN. ACO is
a stochastic search method inspired by the natural be-
havior of ants, which seek the shortest path between
the nest and food, depositing pheromones in the soil
to mark the path other colony members must follow.
ACO exploits a similar mechanism to solve optimiza-
tion problems.
The problem search space is represented as a
graph: the input instances represent the vertices, and
the Euclidean distance between them gives the edges.
Each artificial ant starts from a different instance and
navigates the graph, creating subsets that are submit-
ted to an ML algorithm to evaluate them according to
the achieved performance. The probability of an ant
selecting an instance is based on the heuristic advan-
tage associated with the instance and the amount of
pheromone present Salama et al. (2016). The algo-
rithm returns the best set as the response according to
the best accuracy. Algorithm 1 presents a basic pseu-
docode of the IS with ACO.
This paper implemented the IS algorithm based
on the ACO principles proposed in (Miloud-Aouidate
and Baba-Ali, 2013), called ANT-IS. It was chosen
Installation and use instructions can be found at https:
Data: Any combinatorial problem
Result: Best solution
while stop condition not reached do
Algorithm 1: Basic ACO pseudocode.
because it presents a simple and versatile IS approach
that can be easily adapted to perform attribute se-
lection and allows execution in parallel using sev-
eral processing cores. That article describes the main
steps and equations that indicate ant behavior and the
calculations of an instance’s heuristic advantage
Regarding IS with ACO, works generally aim to re-
duce the computational time for training ML models
Salama et al. (2016), Akinyelu (2020), Gong et al.
(2021), El Bakrawy et al. (2022). Salama et al. (2016)
presented five improved versions of their own ACO-
based IS algorithm, incorporating feature selection.
Akinyelu (2020) applied a threshold detection ap-
proach and IS technique (ACO + kNN) to improve
the speed of big data classification models.
Although the articles mentioned above have
achieved excellent data reduction results, none of the
previous approaches can change the reduction per-
centage. In healthcare databases, generally small and
very unbalanced, adjusting the selection probability
of an instance allows control of the amount of reduc-
tion to which a dataset will be subjected, and, in this
way, we can obtain more customized response sets.
Hott. et al. (2022), in turn, applied IS with ACO
to obtain more efficient classification models in iden-
tifying school performance in arithmetic, reading, and
writing of children and adolescents with hyperactivity
disorder and attention deficit.
As in the previous study, this work also seeks to
apply the IS and ACO techniques in the health con-
text, specifically depression. However, in our article,
the selected instances will be evaluated visually in the
sample space and also regarding their representative
quality within their class. In this context, we are not
reducing the data due to the size of the used database,
, which is not big, the goal here is to reduce it in order
to find a subset of the original database that allows the
A simplified simulation can be seen here https://youtu.
Improving the Instance Selection Method for Better Detection of Depression in Children and Adolescents
training of ML algorithms with a better detection rate
of depression in children and teenagers.
4.1 Dataset
The database used in this study was obtained in part-
nership with the Graduate Program in Psychology:
Cognition and Behavior at the Federal University of
Minas Gerais/Brazil (UFMG). The dataset
377 instances and 75 attributes, with information on
children and adolescents (10 - 16 years old) with dif-
ferent depressive symptoms. In order to adapt the raw
data, it was necessary to submit them to the following
pre-processing procedures.
1. Identification and manipulation of the class at-
tribute. As the database originally received was
not classified, responses to the Childhood Depres-
sion Inventory (CDI) questionnaire were used for
this purpose. Item scores are summed into a total
depression score (CDI Sum), which ranges from
0 to 54. The higher the score, the greater the
chances the patient has a higher depressive state
(Bang et al., 2015). However, the CDI score alone
does not determine the existence or not of de-
pression, but evidence that supports the assess-
ment made by the professional. At this stage, an
instance that did not have CDI information had
to be removed. The literature has no unanimity
regarding the cutoff value determining the divi-
sion between high and low symptomatology. In
this study, Kovacs and Staff (2003)’s recommen-
dation was considered, regarding using the 85th
percentile to indicate high depressive symptoma-
2. Database balancing. “LOW” and “HIGH” symp-
tomatology classes have 314 and 63 individu-
als, respectively. Such an imbalance could inter-
fere with the proposed instance selection process,
tending to obtain better results for the majority
class to the detriment of the minority, the main
target of this study. Therefore, balancing tech-
niques such as oversampling and random subsam-
pling were performed to compare the performance
obtained by IS with and without prior balancing.
3. Training and test set splitting. The database was
divided into two groups, one for model training
A description of all database attributes used as predic-
tors to the ML models can be found on https://docs.googl
and validation, and another for testing, for each of
the proposed balancing scenarios. The divisions
performed can be viewed in Table 1.
Table 1: Number of instances per class for train-
ing/validation and testing.
Class Original Unbalanced Under Over Test
dataset train/val train/val train/val
HIGH 63 48 48 243 15
LOW 314 243 48 243 80
Total 377 291 126 486 86
4.2 IS + ACO, Balancing Methods, and
ML Algorithms
ANT-IS algorithm was implemented in Python v3.10
and the tests were carried out using InstanceSelection
(antcolony-is package v1.0.1) from the TestPyPI in-
dex, with default settings. The p parameter was added
to the algorithm, which controls the degree of stochas-
ticity, allowing adjustment of instance selection prob-
ability. For p = 1 the selection probability is maxi-
mum. For p close to zero the probability is minimum,
directly influencing the size of the reduced set pro-
vided in the ANT-IS output. The experiments used
values of p = 0.3, 0.5 and 0.7.
Regarding the balancing of the original dataset,
two techniques were applied. Undersampling was
done randomly, selecting from the majority class the
same number of instances in the minority class, and
Oversampling using SMOTE (over sampling pack-
age) from the imblearn library package v0.9.1, with
Python v3 default settings. Table 1 describes the final
proportions obtained after each of these steps.
As for the ML algorithms, after the IS performed
by the Ant Colony, the obtained response set was
provided for the training of five classification algo-
rithms to evaluate the performance of the Ant-IS:
1NN, CART, neural network MLP, SVM and RF.
All of them were built using the Scikit-learn library
package version 1.0.2, with default settings. The ex-
periments were performed on Windows 11 operat-
ing system using an Intel(R) Core(TM) i7 processor,
2.60GHz, 16GB of RAM and the PyCharm v2022.1.3
tool. Figure 1 outlines the used methodology.
4.3 Model Quality Assessment Metrics
, Recall
, and F-measure
metrics were
used to assess the quality of the ML models. Preci-
Precision =
Recall =
F Measure =
HEALTHINF 2024 - 17th International Conference on Health Informatics
Figure 1: Selection proceeding and results evaluation.
sion is the rate of instances correctly classified as be-
longing to the class in question out of all those clas-
sified in the class. Recall refers to the percentage of
class instances that were correctly predicted to belong
to the class. The F-measure is a harmonic mean be-
tween Precision and Recall. The training of the ML
models was carried out through a stratified 10-fold
cross-validation method, in which the train-validation
procedure is repeated ten times and the mean value
represents the test result.
We analyzed three balancing test scenarios. 1) IS on
the original unbalanced data, 2) IS over oversampled
data (SMOTE), 3) IS over subsampled data (random
subsampling technique). For this balancing test step,
the selection probability was set to 50% (p = 0.5).
Figure 2 presents the results of the tests carried out.
Regarding the influence of data balancing before in-
stance selection, it is interesting that the ANT-IS algo-
rithm performed well on unbalanced data. It outper-
formed the other balancing techniques in 7 of the 10
possibilities in the F-Measure metric, even not being a
balancing algorithm itself. In the Precision and Recall
metrics, the technique that obtained the best perfor-
mance was subsampling, corroborating the idea of the
positive influence of balanced data in the training of
ML models. Therefore, the other experiments in this
study were conducted on the subsampled database
and the IS performed on it.
The average reduction rate obtained with the pa-
rameter p set to 0.5 (50% probability of selecting an
instance) was 48%, and Table 2 gathers the metrics
evaluated in this condition. Table 2 summarizes the
gain or loss obtained, in percentage points, using the
reduced set given as output from the ANT-IS, com-
pared to the subsampled complete set, for each clas-
sifier. There was a significant gain in some specific
cases, in others, a considerable reduction, and, in oth-
ers, the values remained close, with variations around
0 to 2 percentage points, more or less. Negative val-
ues indicate that there was a reduction in the value of
the evaluated metric. The most impacted algorithm by
the IS was the MLP neural network, in the Precision
metric, for the HIGH symptomatology class, reducing
5.59 percentage points. Neural networks need a sig-
nificant amount of data for their learning, which could
explain the low performance, in this metric concern-
ing the other classifiers. However, the Recall met-
ric rose 8.93 percentage points for the same class and
classifier, providing a harmonic average F-Measure
with a gain of 7.05 percentage points. Although the
results oscillate between good gains and slight reduc-
tions, the objective of the work to better identify de-
pression profiles suggests a more detailed analysis of
the Recall metric, which is also considered a detection
rate. Based on the class of HIGH symptomatology,
the main target of this study, all classifiers obtained
a gain in that metric when using the reduced training
set. Such results indicate that the technique used tends
to better detect these individuals than the results ob-
tained without using it. Regarding the performance
and scalability of ANT-IS, the size of the database
strongly impacts the method’s execution time, as il-
lustrated in Figure 3. The diameter of the circles rep-
resents the number of attributes in each database.
Table 2: Gain/Reduction obtained with ANT-IS (in percent-
age points).
Class Precision Recall F-Measure
1NN HIGH 4,40 5,66 4,69
LOW 2,14 2,38 2,06
CART HIGH 0,67 4,07 1,14
LOW 1,11 -2,00 -1,34
MLP HIGH -5,59 8,93 7,05
LOW 1,40 -0,21 3,43
SVM HIGH -0,51 0,07 -0,59
LOW -0,11 -1,00 -0,76
RF HIGH -0,55 0,10 -0,58
LOW -0,07 -0,98 -0,73
Incorporating the p parameter to ANT-IS allows
us to further improve the metrics of each of the ML
algorithms according to the problem’s context. If a
more significant reduction of data in the IS is needed,
lower p values can be experimented with. If the ob-
jective is to increase the quality of the ML model, it
is possible to reach better metrics with higher values
of p. At this stage, three scenarios were evaluated,
adjusting the p probability of selecting an instance
set to i) 30%, ii) 50% and iii) 70%. It is worth re-
membering that with the increase in the probability of
selecting an instance, there is also an increase in the
Improving the Instance Selection Method for Better Detection of Depression in Children and Adolescents
Figure 2: Results of each experiment on the unbalanced database.
Figure 3: Scalability of the ANT-IS algorithm.
number of instances in the response set of the ANT-
IS algorithm, and consequently, the ML model will be
trained with a more extensive training set. The exper-
iments carried out consisted of running the ANT-IS
on the database with each of the mentioned probabili-
ties and observing the value of the metrics, comparing
them with those obtained when using the subsampled
complete set in the training of the ML algorithm.
Figure 4 represents the impact of varying the prob-
ability of selecting an instance on the classifiers’ met-
rics. For the evaluated database, the KNN, CART and
RF algorithms showed a proportional improvement in
the metrics with the increase in the reduced training
set. In contrast, the SVM algorithm showed the oppo-
site behavior, reducing the gain in the metrics with the
increase in the training set. The MLP neural network,
on the other hand, presented a different and atypical
behavior concerning the others. The objective of this
experiments stage was not to prove which algorithm
obtained the best performance (which would require a
careful adjustment of its hyperparameters) but rather
to identify that they are strongly impacted by the size
of the reduced set, demonstrating the importance of
having a way to adjust the instance selection proba-
HEALTHINF 2024 - 17th International Conference on Health Informatics
Figure 4: Gain/Reduction in metrics due to p variation.
Another important point to be considered is the
identification of the most repeatedly selected in-
stances by the Ant-IS algorithm since, due to its
stochastic characteristic, it presents a different set of
instances as a result of each execution. Figure 5 and
Figure 6 illustrate the selection frequency of each in-
stance in 100 repeated executions of the algorithm on
the analyzed database.
Figure 5: Frequency of instance selection by Ant-IS in 100
In Figure 5, each point identifies an instance duly
represented in the sample space, associated with its
symptom classification. The selection covered the
sample space well, not concentrating on any specific
area. This feature of ANT-IS avoids getting stuck in
local minima in the search space. It is also noted,
through Figure 6, that there was a slight tendency for
Ant-IS to select instances of the LOW symptomatol-
ogy class. Of the 57 instances selected more than half
the time, 36 belonged to the LOW symptomatology
class and 21 to the HIGH one.
Moving on to a more specific analysis, based on
the 3 instances that were most selected in their re-
spective classes, Table 3 describes their most relevant
attributes. There are three male and three female indi-
viduals, aged between 11 and 15 years old. Three are
LOW class and three are HIGH class. Three were re-
ceiving regular psychological care, two of them with
HIGH symptomatology. Only one of the instances has
the parents split up, and it is a LOW symptomatol-
ogy instance. The combination of attributes of HIGH-
class instances presents medium-high values concern-
ing others, for anxiety, social problems, and conduct,
in addition to negative or self-defeating thoughts.
Other striking features of instances of HIGH symp-
tomatology are the presence of oppositional defiant
disorder, high aggressiveness, difficulty paying at-
tention and externalizing disorders. Such conditions
were observed by a psychology professional at the
time of collecting information from children and ado-
lescents in this study, based on the Diagnostic and
Statistical Manual of Mental Disorders V (APA et al.,
2014) and the applied CDI and YSR questionnaires.
The last line of Table 3 presents the minimum and
maximum values existing in the evaluated database,
to help the comparative analysis.
About the instances of the LOW symptomatology
class, except for some specific attributes with higher
values (negative thinking and anxiety with high values
in one of them), the other attributes remained close to
the lower limits of the analyzed sample. However,
such symptoms may represent warning signs for this
individual in question. Regarding the time spent with
the parents, in all six instances, the number of hours
spent with the mothers was slightly higher than those
spent with the fathers, or the same in a few cases. All
of them reported that the father worked outside the
home, and only two reported that the mother did not
work, one from the LOW class and the other from the
HIGH symptomatology class.
The results of this study corroborate others in the
literature in the sense that depression can be associ-
ated with other psychiatric disorders and comorbidi-
ties. According to Maughan et al. (2013), two-thirds
of adolescents with depression have at least one co-
morbid psychiatric disorder, and 10-15% have two
Improving the Instance Selection Method for Better Detection of Depression in Children and Adolescents
(a) TOP5 most selected instances (appeared in 65 out of 100 ANT-IS runs); (b) TOP15 (were selected in 60 out of 100
executions); (c) TOP30 (appeared in 55% of runs) and (d) instances that were selected in 50% of ANT-IS runs.
Figure 6: Separation by selection frequency bands.
Table 3: Relevant attributes of the 3 most selected instances in each class.
Class BR ED Aggr OD CP Age S PS SP Anx Withdr NT AD SC SF
LOW 50 47 52 51 50 15 M No 54 69 54 77 50 67 67
LOW 50 40 50 50 50 11 F No 51 54 55 50 51 60 67
LOW 50 44 50 51 50 14 F Yes 58 56 50 50 60 68 66
HIGH 57 72 87 76 67 12 F Yes 58 66 55 73 73 68 67
HIGH 58 62 66 61 62 14 M No 75 74 60 55 54 58 61
HIGH 60 66 68 61 64 13 M Yes 63 70 57 60 73 64 61
Min-Max 50-79 29-80 50-89 50-80 50-78 10-16 - - 50-90 50-100 50-100 50-95 50-100 50-90 0-100%
BR = Break Rules. ED = Externalizing Disorders. Aggr = Aggressiveness. OD = Oppositional defiant disorder.
CP = Conduct problems. S = Sex. PS = Psychological support. SP = Social problems. Anx = Anxiety. Withdr = Withdrawal.
NT = Negative thinking. AD = Attention difficulty. SC = Somatic complaints. SF = Selection frequency.
Figure 7: Comparison between ACO and Genetic Algo-
rithm (GA) approaches for IS according to the F-Measure
or more associated comorbidities. Adolescents with
depression are more likely to have anxiety and more
likely to also have a disruptive behavior disorder com-
pared to those who are not depressed. The three most
selected instances of HIGH symptomatology showed
this picture of associated disorders.
Finally, regarding comparing the ACO with other
approaches for IS, we compared it with the Genetic
Algorithm (GA) heuristic (Santana, 2021) in this arti-
cle. Figure 7 shows that, for the depression database
analyzed in seven ML algorithms (RF, SVM, Logis-
tic Regression, Adaboost, XGBoost, Decision Tree,
and MLP), the F-Measure metric remained very close
in both heuristics, with the ACO heuristic taking only
1/6 of the time spent by the GA for IS on average, pre-
senting practically the same reduction rate (48% and
49% for ACO e GA, respectively).
With regard to obtaining more assertive classification
models, the technique employed proved to be satis-
factory, given the average reduction of 48% in the
size of the original data and the increase in the Re-
call detection rate (between 0.07 and 8.93 percentage
points depending on the ML algorithm evaluated) for
the HIGH symptomatology class, the main target of
this study. This fact could indicate that the selected
instances would be the ones that best characterize the
symptomatology of depressive disorder in children
and adolescents for the analyzed database, in terms
of classification. However, the experiments showed a
tendency of the algorithm to select more LOW symp-
tomatology instances, even with the training data bal-
ancing. Thus, the technique still needs further im-
provement, more tests and support from the analysis
of a psychology professional in evaluating the results
The use of only one specific database makes it
impossible, in principle, to generalize the results
achieved by the algorithm to other contexts. Another
HEALTHINF 2024 - 17th International Conference on Health Informatics
point of attention is the more careful adjustments of
the employed ML algorithms’ parameters that were
instantiated with their general typical values. Better
adjustments could achieve different results. It is also
worth mentioning the small size of the base evaluated,
which can directly influence the quality of the models
generated and the results achieved.
As for future work, three main points need to be
worked on: optimizing the algorithm’s performance,
since preliminary tests on larger databases proved to
be still too slow; expanding the number of databases
tested, including others of different sizes, both bal-
anced and unbalanced, to investigate the balancing ca-
pacity of ANT-IS better and generalize its use; and
finally validate the attribute selection introduced to
the instance selection algorithm, evaluating whether
its application produces any improvement in classi-
fication metrics, favoring its application in big data
The authors thank the National Council for Scien-
tific and Technological Development of Brazil (CNPq
- Conselho Nacional de Desenvolvimento Cient
e Tecnol
ogico Code: 311573/2022-3), the Pon-
ıcia Universidade Cat
olica de Minas Gerais
PUC-Minas, the Coordination for the Improvement
of Higher Education Personnel - Brazil (CAPES
Grant PROAP 88887.842889/2023-00 PUC/MG,
Grant PDPG 88887.708960/2022-00 PUC/MG -
atica and Finance Code 001), and the Foun-
dation for Research Support of Minas Gerais State
(FAPEMIG – Code: APQ-03076-18).
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