Predicting Depression in Children and Adolescents using the SHAP

Approach

Marcelo Balbino

1,2

, Renata Santana

, Maycoln Teodoro

, Mark Song

, Luis Z

arate

and Cristiane Nobre

Department of Computing and Civil Construction, Federal Center for Technological Education of Minas Gerais, Brazil

Department of Computing, Pontiﬁcal Catholic University of Minas Gerais University, Brazil

Department of Psychology, Federal University of Minas Gerais, Brazil

Keywords:

Depression, Machine Learning, Interpretability, SHAP.

Abstract:

Depression is a disease with severe consequences that affects millions of people, with the onset of the ﬁrst

symptoms being common in youth. It is essential to identify and treat individuals with depression as early

as possible to prevent the losses caused by the disorder throughout life. However, the diagnostic criteria of

depressive disorders for children/adolescents or adults is not differentiated, even though authors claim that the

particularities of childhood must be considered. This may be why childhood depression is being underdiag-

nosed. Therefore, this work aims to discover the most signiﬁcant features in diagnosing depression in children

and adolescents through Machine Learning methods and the SHAP approach. Models with Machine Learning

algorithms were developed, and the model with SVM presented the best results. The application of SHAP

proved to be fundamental to deepen the understanding of this model. The experiments indicated that feelings

of isolation, sadness, excessive worry, complaints about one’s appearance, resistance to academic tasks, and

the mother’s schooling are the most signiﬁcant features in predicting depression in children and adolescents.

Such results can help to understand depression in these individuals and thus lead to appropriate treatment.

1 INTRODUCTION

Depression is a term used to refer to Depressive Dis-

orders, being understood as a pathology that alters

and compromises the body and mind, mainly affect-

ing mood. The individual with Depressive Disorders

may have persistent sadness, lack of interest or plea-

sure in previously rewarding activities, loss of con-

ﬁdence and self-esteem, unjustiﬁed feelings of guilt,

ideas of death and suicide, sleep and appetite distur-

bances, fatigue, poor concentration, and symptoms of

anxiety. Its effects can be long-lasting or recurrent

and can affect a person’s ability in essential areas of

functioning (APA et al., 2013; WHO, 2017).

From 2005 to 2015, there was an 18% increase

in people with depression worldwide, resulting in

more than 300 million people (WHO, 2017). Fur-

thermore, it is estimated that one in six people (about

16.67%) will suffer from depression at some point in

their lives, which means more than one billion people

worldwide affected by the disorder (APA, 2017).

Studies indicate that Depressive Disorders have

been the leading cause of illnesses and disabilities in

adolescence (WHO, 2017). In addition, half of the

people who develop mental disorders experience the

ﬁrst symptoms by 14 years (Yoon et al., 2014). There-

fore, it is essential to identify and treat individuals

with depression in childhood/adolescence to prevent

the losses caused by the disorder throughout life.

The deﬁnition of depression in youth is not specif-

ically addressed in the Diagnostic and Statistical

Manual of Mental Disorders (APA et al., 2013). There

is no differentiation of diagnostic criteria for depres-

sive disorders for children, adolescents, or adults.

Nevertheless, authors claim that the peculiarities of

childhood must be considered in the assessment and

diagnosis of depression in children (Quevedo et al.,

2018; Bernaras et al., 2019).

However, one of the obstacles to treating depres-

sion is its assessment and diagnosis, leading to a lack

of treatment or inadequate handling of it (Pavlova and

Uher, 2020). This scenario highlights the importance

of instruments that can support the correct diagnosis.

A survey gave rise to a database containing infor-

mation on 377 children and adolescents with different

depressive symptomatology.

514

Balbino, M., Santana, R., Teodoro, M., Song, M., Zárate, L. and Nobre, C.

Predicting Depression in Children and Adolescents using the SHAP Approach.

DOI: 10.5220/0010842500003123

In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 5: HEALTHINF, pages 514-521

ISBN: 978-989-758-552-4; ISSN: 2184-4305

Given the high incidence rate of depression and its

peculiarities in children and adolescents, it is essential

to explore the dataset created by that study, making it

a source of knowledge that can support the diagnosis

and actions to help individuals in this age group.

In this context, Machine Learning (ML) meth-

ods are adequate to the mentioned problem as they

have achieved satisfactory results in the extraction of

knowledge from databases. More speciﬁcally, ML

systems have been shown to be adequate in support-

ing the diagnosis of diseases (Rav

ı et al., 2017).

Advances in the ML area have provided classiﬁ-

cation models with high predictive capacity. How-

ever, in many contexts, it is critical to understand the

model’s decisions. This understanding increases the

knowledge of the problem studied and generates con-

ﬁdence in the results (Karim et al., 2018). Therefore,

in scenarios such as the present work, it is necessary

to provide the interpretability of the models. For this

purpose, an alternative is to use SHapley Additive ex-

Planations (SHAP) (Lundberg and Lee, 2017).

SHAP is an approach to interpreting predictive

models based on cooperative game theory that helps

explain ML models (Lundberg and Lee, 2017). The

approach has resources that allow understanding the

effects of features in individual predictions and their

importance for each class in a classiﬁcation problem.

Therefore, this work aims to discover, through ML

methods and the SHAP approach, which are the most

signiﬁcant characteristics in diagnosing depression in

children and adolescents.

Predictive models were developed based on four

ML methods: Decision Tree, Neural Networks, Sup-

port Vector Machines (SVM), and Random Forest.

The SVM-based model achieved the best perfor-

mance, and therefore, SHAP was applied to it.

We believe that the results achieved in this re-

search can help family members, educators, and

health professionals to identify and direct the treat-

ment of children and adolescents with depression,

which is a considerable contribution given the sever-

ity and number of people affected. In addition, the im-

portance of interpretability in problems of this nature

is outstanding, mainly through the SHAP resources.

2 BACKGROUND

2.1 Depression in Children and

Adolescents

Literature has recognized the importance and pres-

ence of depressive conditions in childhood and ado-

lescence (Bahls, 2002; Schwan and Ramires, 2017;

Bernaras et al., 2019). This recognition is relevant

since the need for adequate intervention can prevent

further problems.

According to De Haan et al. (2019), childhood de-

pression is a growing problem, and its main symp-

toms are low self-esteem, fears, sleep disturbances,

enuresis, sadness, abdominal pain, guilt, fatigue, lack

of interest in activities in general, suicidal ideation,

and problems of learning. The causes are related, in

most studies, to psychosocial aspects, that is, loss of

emotional bonds, parental divorce, physical and psy-

chological violence, lack of family support.

Zavaschi et al. (2002) indicate a relationship be-

tween childhood losses and adult depression. Ac-

cording to the authors, children who suffered breaks

in meaningful bonds tended to develop depressive

pathology as adults.

For Bahls (2002), falling school performance is

one of the ﬁrst indicators of depression in children, in

addition to the development of dysphoria, isolation,

and sadness. Furthermore, some symptoms can be

“recombined” with each other, implying difﬁculties

in diagnosing childhood depression. Symptoms such

as apathy, weight loss, school refusal, and reduced ap-

petite may be signiﬁcant for speciﬁc diagnoses.

Although the diagnostic criteria are the same as

for adults, in children with depressive disorder, symp-

toms such as anxiety, somatic complaints, and hallu-

cinations are predominant. In adolescents, changes in

sleep and appetite are common. Some authors point

out that suicidal ideation and suicide attempts appear

as aggravation of symptoms (Quevedo et al., 2018).

Even though depression is evaluated as one of the

most frequent problems in clinical practice, it is con-

sidered that it has been underdiagnosed and, as a re-

sult, few patients receive treatment (Bernaras et al.,

2019). Diagnosis can be difﬁcult due to comorbidi-

ties present in depressive conditions. Perhaps because

of this, the symptoms manifest themselves obscurely,

making it impossible or difﬁcult to recognize this

condition in children (Scivoletto and Tarelho, 2002).

Many times the children themselves have difﬁculties

in identifying and externalizing their symptoms. Still,

parents and professionals identify problems that ini-

tially are not recognized as depression since there

are organic complaints such as headache, abdominal

pain, and diarrhea, which make the referred diagnosis

difﬁcult (Abela and Hankin, 2008).

2.2 Model Interpretability

Complex ML methods (e.g., Random Forest and Neu-

ral Networks) generally have higher predictive perfor-

Predicting Depression in Children and Adolescents using the SHAP Approach

515

mance than traditional models, which are simpler and

more interpretable (e.g., Linear Regression and Deci-

sion Tree). However, specialists need to understand

and trust these models in health-related topics, which

is often impossible due to the lack of intuition and in-

terpretation of their predictions (ElShawi et al., 2020).

In addition, ML methods are often used in sci-

entiﬁc research and, therefore, must not only clas-

sify or predict but also answer the “how” and “why”

questions to be consistent with the science objectives.

Consequently, ML models and artiﬁcial intelligence

have been used to increase human understanding of

different real-world problems (Karim et al., 2018).

Faced with the need to explain the results gener-

ated by ML models, the scientiﬁc community began

to turn its attention to the design of methods aimed

at interpretability. For that, one can make use of in-

trinsically interpretable models, which have presented

limitations in speciﬁc scenarios, or methods that pro-

vide post-hoc explanations for the predictions made

by complex models (Kaur et al., 2020).

As illustrated in Figure 1, interpretability meth-

ods introduce a new perspective to ML solutions by

adding an explanation model(g) to the original pre-

diction model( f ). The purpose of these methods is to

present an interpretable approximation of the original

model (Lundberg and Lee, 2017).

Figure 1: How an explanation model is used in predicting

interpretation (Mokhtari et al., 2019).

In this sense, Lundberg and Lee (2017) present

SHAP, a framework to interpret ML models. The ap-

proach uses SHAP values as a way to measure the im-

portance of each feature in a prediction. In this way,

each feature impacts a given instance by a value that

can be positive, negative, or zero. As illustrated in

Figure 2, the sum of the effects of all features corre-

sponds to an approximation of prediction f (x) to be

explained. In this case, φ

, φ

increase the predic-

tion value, while φ

decreases the same value. The

values of φ

explain how to get from the base value

E[ f (x)], which would be the model’s prediction if no

feature was known for the output f (x) (Lundberg and

Lee, 2017; Mokhtari et al., 2019).

Figure 2: Overview of the SHAP approach (Mokhtari et al.,

2019).

3 RELATED WORKS

We sought to survey research that uses ML for age-

independent depression prediction or other methods

used in works related to depression in children and

adolescents.

Patel et al. (2015) conducted a survey of 68 in-

dividuals applying ML methods to predict late de-

pression and treatment response. The models used

demographic data, cognitive skills, and brain charac-

teristics acquired by multimodal Magnetic Resonance

Imaging (MRI). Among the tested methods, decision

trees estimated the most accurate models for the di-

agnosis of late depression (accuracy of 87.27%) and

response to treatment (accuracy of 89.47%).

In another study, 33 adolescents underwent struc-

tural MRI and were followed for ﬁve years to mon-

itor the appearance of clinically signiﬁcant depres-

sive symptoms. With an SVM-based model, the

researchers tested whether basal cortical thickness

could distinguish adolescents who develop depression

from those who remain free of any disorder. The

research concluded that basal cortical thickness cor-

rectly predicted the future onset of depression with an

accuracy of 70% (Foland-Ross et al., 2015).

Wu et al. (2015) investigated the utility of mul-

tiple neuromorphometric indices (neuron imaging) in

differentiating pediatric patients with unipolar depres-

sion from healthy controls. Scans of 51 depressive

and healthy patients were used, and a model using

SVM was trained to classify the individuals. The

model correctly identiﬁed 40 of the 51 individuals.

The results show that multiple neuromorphometric

indices can qualify as a diagnostic marker for pedi-

atric unipolar depression. The work also identiﬁed

the most relevant neuromorphometric characteristics

in distinguishing between pediatric unipolar depres-

sion patients and healthy controls.

Yang et al. (2016) used the decision tree for de-

pression classiﬁcation from the scores of the Patient

Health Questionnaire (PHQ-8) and the characteris-

tics of the participants (PTSD-Depression Diagnos-

tic, sleep-status, feeling, and personality) obtained

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516

through the analysis of transcript ﬁles of consulta-

tions. The model got an F1-score of 0.571 for the

depressed class and 0.877 for the non-depressed class.

A study based on a sample of 386 adolescent stu-

dents from 5 public schools in Tunisia sought to iden-

tify factors associated with depression and the co-

occurrence with symptoms of anxiety disorders. Data

were collected through a sociodemographic question-

naire and the Beck Depression Inventory. Logistic re-

gression analysis showed an association between de-

pression and female gender, 1st and 4th grades, low

and medium socioeconomic status, school dissatisfac-

tion, and anxiety disorders (Sendi et al., 2018).

Vinnakota and Kaur (2018) evaluated the associ-

ation between depression and internalizing and ex-

ternalizing behaviors in 150 adolescents residing in

institutional homes in India. The PHQ-9 was used

to track depression, and the Strengths and Difﬁcul-

ties Questionnaire was used to score behaviors. The

prevalence of depression was found in 12.7% of ado-

lescents. Depression was signiﬁcantly associated

with gender and academic achievement. A correlation

was also found between internalizing and externaliz-

ing behaviors with clinical depression.

The use of ML or Statistics methods in the con-

text of depression is noticed, including considering

adolescents. In the studies surveyed involving ML,

there is a prevalence of imaging exams as an input

to the model. However, none of the works found in-

cluded the use of interpretability tools as proposed in

this work. There are works related to the interpretabil-

ity of ML models in different scenarios, including in

the health area (ElShawi et al., 2020; Karim et al.,

2019), but we did not ﬁnd any that resemble the pro-

posal presented here.

4 METHOD

4.1 Database Description

The database used in this study contains information

on children and adolescents between 10 and 16 years

old, being 158 male and 219 female, totaling 377 in-

stances with different depressive symptomatology.

The database has 75 attributes that include demo-

graphic and social characteristics, as well as scores

obtained from the Children’s Depression Inventory

(CDI) and Young Self Report (YSR) inventories. In

addition, other issues considered necessary by the

mental health community were also included, espe-

cially factors such as anxiety, social problems, lack of

attention, aggression, behavior problems (APA et al.,

2013).

We recognize that the dataset used in this work

does not contain the ideal number of instances. How-

ever, as Mena and Gonzalez (2006) and Pasini (2015)

state, except in extensive epidemiological studies, the

complexity and high cost of experiments usually re-

strict the number of samples available.

4.2 Predicion Models Development

The dataset was preprocessed to ﬁt the selected algo-

rithms better and obtain more consistent models. In

general terms, the following steps were performed:

• Removal of attributes with more than 70% miss-

ing data. In the other missing data, the mode and

the mean were applied as statistical measures to

ﬁll in the data;

• Handling of data inconsistencies;

• Numerical encoding of nominal features;

• Binarization of the values of some features;

• Discretization of the values of some features;

• The dimensionality reduction of the dataset was

performed using a genetic algorithm (GA) to im-

prove the models’ performance. We chose Non-

Dominated Sorting Genetic Algorithm II (NSGA-

II) to ﬁnd the best subset of features maximiz-

ing its ﬁtness, in this case, the F-measure. The

K-Nearest Neighbors (KNN) classiﬁer was used

to measure the F-measure. The GA was imple-

mented in the Python language, using the DEAP

library. GA selected 34 features for the model;

• Normalization of data in the interval [0,1] to pre-

vent different scales in the values of the features

from impairing the model;

• Initially, the dataset did not include an attribute

to classify individuals by symptomatology. For

this, we obtained the CDI Sum as a depression

score equivalent to the sum of the items present

in the CDI inventory. The CDI Sum can range

from [0, 54]. This score is not sufﬁcient to deter-

mine the existence of the disorder in the individual

but may support the assessment made by a profes-

sional. Based on the recommendation by Kovacs

(Kovacs, 2003), we used the 85th percentile for

high symptomatology, obtaining 63 classiﬁed as

“High” symptomatology and 314 as “Low”;

• Random separation of 15% of the instances of

each class to perform the testing step;

• Balancing the remaining 85% of the data us-

ing the SpreadSubsample algorithm present in the

WEKA tool (Singhal and Jena, 2013). Table 1

shows the number of instances reserved per class

for training/validation and testing.

Predicting Depression in Children and Adolescents using the SHAP Approach

517

Table 1: Number of instances for training and testing sets.

Class

Preprocessed

Database

Model

Creation

Test

HIGH 63 53 10

LOW 314 53 50

Total 377 106 60

To obtain better predictive capacity, models based

on four ML algorithms were developed: Decision

Tree, Neural Networks, SVM, and Random Forest.

The models were implemented in Python using the

Scikit-learn library. The Precision, Recall, and F-

measure metrics were used to assess the quality of the

models. All classiﬁers were built and validated using

the k-fold cross-validation process, with k = 10.

The SHAP approach was used only with the best-

performing classiﬁcation model. As shown in Section

2.2, SHAP generates an explanation model from the

classiﬁcation model. This explanation model can re-

ceive a sample of data as input and identify the fea-

tures that impacted each prediction of this sample.

We chose to apply the explanation model on the same

set of instances in which the classiﬁcation model tests

were applied. As a result, SHAP generates plots that

deepen model understanding and identify the most

relevant features in predicting depression.

5 RESULTS

Models were generated with different ML algorithms

aiming at classifying children and adolescents as to

their symptoms. Figure 3 presents the results of the

test phase of the prediction models. Note that for

“Low” symptomatology, all models had an expressive

performance, with slightly superior performance for

the model using SVM with an F-measure of 98%. In

predicting “High” symptomatology, the SVM-based

model obtained the best performance with an F-

measure of 90%. That is why we developed the in-

terpretability with SHAP for this model.

Figure 3: Models performance evaluation.

The features mentioned in the description of the

experiments are listed in Table 2, as well as the nu-

merical transformation of their values. It is notewor-

thy that the values referring to the features were nor-

malized, which can make it difﬁcult to read the data.

Figure 4 corresponds to one of the plots generated

by SHAP, called Summary Plot, which presents an

overview of the most signiﬁcant features in predict-

ing depression symptomatology. This plot orders the

features by their importance. Summary Plot uses the

SHAP values to show the distribution of impacts that

each feature has on the model’s output. Each point

present on the line for a given feature represents a

child or adolescent impacted by this feature. Suppose

a point is on the right side of the central axis. In that

case, this feature inﬂuences the individual towards the

“High” symptomatology class. The farther away from

the central axis, the more signiﬁcant the impact of that

feature on that individual. Likewise, if the instance

is on the left, the same feature moves it towards the

“High” symptomatology class. It is noteworthy that

the inﬂuence of a feature alone does not deﬁne the in-

dividual’s class. The point color represents the value

of the feature in the instance, red to high values and

blue to low values.

Regarding the explanations that we can extract for

the problem in question, it is remarkable that CDI20

is the feature of a more signiﬁcant inﬂuence in pre-

dicting an individual’s symptomatology for depres-

sion. Thus, for the model, the feeling of loneliness

is what most evidence depression.

Other CDIs appear in the sequence, such as CDI11

which highlights excessive levels of worry as a promi-

nent inﬂuencing factor for prediction as “High” symp-

tomatology, which especially when dealing with a

child or adolescent, should not be typical. The CDI14

and CDI25 are related to low self-esteem of individ-

uals prone to depression. The CDI15 highlights the

lack of motivation with school activities as another

prominent inﬂuencing factor for the “High” symp-

tomatology. Still, with an important inﬂuence is the

CDI1 related to constant sadness.

The explanation model also highlights a relation-

ship between the mother’s schooling level and pre-

dicting symptoms in children and adolescents. The

plot indicates a positive impact for mothers with com-

plete graduate. We believe that this feature may relate

to the more signiﬁcant support and conﬁdence these

individuals receive from their mothers. It is important

to emphasize that the inﬂuence of the father’s educa-

tion on the child’s depression situation was not ob-

served in the experiments carried out.

Park et al. (2013) obtained similar results. The

authors concluded that mothers with a higher level

of education might have more conﬁdence in dealing

with the difﬁculties arising from child-rearing. Such

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518

Table 2: Features highlighted in the explanation models.

Feature Domain

CDI1

I get sad from time to time (0)

I am often sad (0.5)

I’m always sad (1)

CDI11

I get worried from time to time (0)

I get worried often (0.5)

I always feel worried (1)

CDI14

I am satisﬁed with my appearance (0)

My appearance has some downsides (0.5)

I’m ugly (1)

CDI15

Doing homework is not a big problem for me (0)

I am often pressured to do my homework (0.5)

I have to force myself to do my homework (1)

CDI20

I don’t feel alone (0)

I feel alone often (0.5)

I always feel alone (1)

CDI24

I am as good as the other children (0)

If I want, I can be as good as the other children (0.5)

I can’t be as good as the other children (1)

CDI25

I’m sure someone loves me (0)

I’m not sure if anyone loves me (0.5)

Nobody really likes me (1)

School M Complete Graduation No (0), Yes (1)

ADH problems DSM

(Attention Deﬁcit and Hyperactivity)

[0, 1]

Anxiety depression [0, 1]

Figure 4: Impact of features on depression symptomatology.

an attitude of greater conﬁdence and a sense of self-

mastery serves as a role model for their children. The

authors also observed that the father’s education level

had no impact on the children’s depression.

Figure 5 is another SHAP plot called Force plot

which allows us to visualize an individual prediction.

The features that lead the prediction for the “High”

class are shown in red. Those that distance the pre-

diction for this same class are in blue.

Predicting Depression in Children and Adolescents using the SHAP Approach

519

Figure 5: Individual predictions.

Figure 5 (a) shows a child or adolescent predicted

to be “ Low” symptomatology. The absence of prob-

lems related to loneliness, excessive worry, academic

issues, self-esteem, or acceptance of appearance con-

tribute to the prediction as “Low” symptomatology.

However, the mother’s level of education has a nega-

tive impact.

Figure 5 (b) exempliﬁes an individual with “High”

symptomatology prediction. In this case, feelings

of loneliness, negative evaluation of one’s appear-

ance, and the presence of comorbidities of Attention-

Deﬁcit/Hyperactivity and Depression and Anxiety are

the main issues that led to the prediction as “High”

symptomatology. The individual has a positive as-

sessment regarding schoolwork, but not enough to

change the prediction.

6 CONCLUSIONS

This research sought to recognize the proﬁle of chil-

dren and adolescents with depression, identifying the

most signiﬁcant features for predicting their symp-

tomatology. The studies highlighted the theme’s rel-

evance, given the number of people affected and the

severity of the consequences related to depressive dis-

orders. Considering that such conditions often start in

youth, it is essential to identify them as early as pos-

sible to prevent the damage caused from continuing.

Some ML methods were tested to predict the indi-

vidual’s symptomatology and the SVM proved to be

the most suitable for the scenario in question. Further-

more, the SHAP approach proved essential for under-

standing model decisions and highlighting the most

important features.

In general terms, the experiments indicated that

feelings related to isolation, sadness, excessive worry,

complaints about one’s appearance, and resistance

to academic tasks are the most signiﬁcant features

in predicting depression symptomatology in children

and adolescents. Therefore, we understand that such

feelings and actions deserve attention from those

close to young people when perceived at excessive

levels. On the other hand, the explanation model

highlighted the mother’s schooling, with a positive

inﬂuence for mothers with higher schooling levels,

highlighting the importance of family care.

ACKNOWLEDGEMENTS

The authors thank the National Council for Scientiﬁc

and Technological Development of Brazil (CNPq -

Conselho Nacional de Desenvolvimento Cient

ıﬁco e

Tecnol

ogico), the Coordination for the Improvement

of Higher Education Personnel - Brazil (CAPES), the

Foundation for Research Support of Minas Gerais

State (FAPEMIG), the Federal Center for Techno-

logical Education of Minas Gerais (CEFET-MG), the

Federal University of Minas Gerais (UFMG), and the

Pontiﬁcal Catholic University of Minas Gerais (PUC

Minas).

REFERENCES

Abela, J. R. and Hankin, B. L. (2008). Handbook of depres-

sion in children and adolescents. Guilford Press, New

York, NY, USA.

APA (2017). Depression. https://www.psychiatry.

org/psychiatrists/practice/quality-improvement/

quality-measures-for-mips-quality-category.

APA, A. P. A. et al. (2013). Diagnostic and statistical man-

ual of mental disorders (DSM-5®). American Psychi-

atric Pub, Washington, D.C.

Bahls, S.-C. (2002). Aspectos cl

ınicos da depress

em crianc¸as e adolescentes. Jornal de Pediatria,

78(5):359–366.

Bernaras, E., Jaureguizar, J., and Garaigordobil, M. (2019).

Child and adolescent depression: a review of theo-

HEALTHINF 2022 - 15th International Conference on Health Informatics

520

ries, evaluation instruments, prevention programs and

treatments. Frontiers in Psychology, 10:543.

De Haan, A., Landolt, M., Fried, E., Kleinke, K., Alisic,

E., Bryant, R., Salmon, K., Chen, S.-H., Liu, S.-T.,

Dalgleish, T., et al. (2019). Dysfunctional posttrau-

matic cognitions, posttraumatic stress, and depression

in children and adolescents exposed to trauma: A net-

work analysis. Journal of Child Psychology and Psy-

chiatry.

ElShawi, R., Sherif, Y., Al-Mallah, M., and Sakr, S. (2020).

Interpretability in healthcare: A comparative study

of local machine learning interpretability techniques.

Computational Intelligence.

Foland-Ross, L. C., Sacchet, M. D., Prasad, G., Gilbert, B.,

Thompson, P. M., and Gotlib, I. H. (2015). Cortical

thickness predicts the ﬁrst onset of major depression

in adolescence. International Journal of Developmen-

tal Neuroscience, 46:125 – 131.

Karim, A., Mishra, A., Newton, M., and Sattar, A. (2018).

Machine learning interpretability: A science rather

than a tool. arXiv preprint arXiv:1807.06722.

Karim, M. R., Cochez, M., Beyan, O., Decker, S., and

Lange, C. (2019). Onconetexplainer: explainable pre-

dictions of cancer types based on gene expression

data. In 2019 IEEE 19th International Conference

on Bioinformatics and Bioengineering (BIBE), pages

415–422. IEEE.

Kaur, H., Nori, H., Jenkins, S., Caruana, R., Wallach, H.,

and Wortman Vaughan, J. (2020). Interpreting inter-

pretability: Understanding data scientists’ use of in-

terpretability tools for machine learning. In Proceed-

ings of the 2020 CHI Conference on Human Factors

in Computing Systems, pages 1–14.

Kovacs, M. (2003). Children’s Depression Inventory (CDI):

Technical Manual Update. Multi-Health Systems, In-

corporated.

Lundberg, S. M. and Lee, S.-I. (2017). A uniﬁed approach

to interpreting model predictions. In Proceedings of

the 31st international conference on neural informa-

tion processing systems, pages 4768–4777.

Mena, L. J. and Gonzalez, J. A. (2006). Machine learning

for imbalanced datasets: Application in medical diag-

nostic. In Flairs Conference, pages 574–579.

Mokhtari, K. E., Higdon, B. P., and Bas¸ar, A. (2019). In-

terpreting ﬁnancial time series with shap values. In

Proceedings of the 29th Annual International Confer-

ence on Computer Science and Software Engineering,

pages 166–172.

Park, A. L., Fuhrer, R., and Quesnel-Vall

ee, A. (2013). Par-

ents’ education and the risk of major depression in

early adulthood. Social Psychiatry and Psychiatric

Epidemiology, 48(11):1829–1839.

Pasini, A. (2015). Artiﬁcial neural networks for small

dataset analysis. Journal of thoracic disease,

7(5):953.

Patel, M. J., Andreescu, C., Price, J. C., Edelman, K. L.,

Reynolds, C. F., and Aizenstein, H. J. (2015). Ma-

chine learning approaches for integrating clinical and

imaging features in late-life depression classiﬁcation

and response prediction. International Journal of

Geriatric Psychiatry, 30(10):1056–1067.

Pavlova, B. and Uher, R. (2020). Assessment of psy-

chopathology: Is asking questions good enough?

JAMA psychiatry, 77(6):557–558.

Quevedo, J., Nardi, A. E., and da Silva, A. G. (2018). De-

press

ao-: Teoria e Cl

ınica. Artmed Editora, Brasil.

Rav

ı, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-

Perez, J., Lo, B., and Yang, G.-Z. (2017). Deep learn-

ing for health informatics. IEEE journal of biomedical

and health informatics, 21(1):4–21.

Schwan, S. and Ramires, V. R. R. (2017). Depress

ao em

crianc¸as: Uma breve revis

ao de literatura. Psicologia

Argumento, 29(67).

Scivoletto, S. and Tarelho, L. G. (2002). Depress

na inf

ancia e adolesc

encia. RBM rev. bras. med,

59(8):555–558.

Sendi, I., Chouikh, A., Ammar, A., and Bouaﬁa, N. (2018).

Depression in a sample of tunisian adolescents: preva-

lence, associated factors and comorbidity with anx-

iety disorders. International journal of adolescent

medicine and health.

Singhal, S. and Jena, M. (2013). A study on weka tool for

data preprocessing, classiﬁcation and clustering. In-

ternational Journal of Innovative technology and ex-

ploring engineering (IJItee), 2(6):250–253.

Vinnakota, A. and Kaur, R. (2018). A study of depres-

sion, externalizing, and internalizing behaviors among

adolescents living in institutional homes. Interna-

tional Journal of Applied and Basic Medical Re-

search, 8(2):89.

WHO, W. H. O. (2017). Depression and other common

mental disorders: global health estimates. http://www.

who.int/iris/handle/10665/254610.

Wu, M.-J., Wu, H. E., Mwangi, B., Sanches, M., Selvaraj,

S., Zunta-Soares, G. B., and Soares, J. C. (2015). Pre-

diction of pediatric unipolar depression using multiple

neuromorphometric measurements: A pattern classi-

ﬁcation approach. Journal of Psychiatric Research,

62:84 – 91.

Yang, L., Jiang, D., He, L., Pei, E., Oveneke, M. C., and

Sahli, H. (2016). Decision Tree Based Depression

Classiﬁcation from Audio Video and Language Infor-

mation. In Proceedings of the 6th International Work-

shop on Audio/Visual Emotion Challenge - AVEC ’16,

AVEC ’16, pages 89–96, New York, NY, USA. ACM.

Yoon, S., Taha, B., and Bakken, S. (2014). Using a data

mining approach to discover behavior correlates of

chronic disease: A case study of depression. In Stud-

ies in Health Technology and Informatics, volume

201, pages 71–78.

Zavaschi, M. L. S., Satler, F., Poester, D., Vargas, C. F., Pi-

azenski, R., Rohde, L. A. P., and Eizirik, C. L. (2002).

Associac¸

ao entre trauma por perda na inf

ancia e de-

press

ao na vida adulta. Revista brasileira de psiquia-

tria= Brazilian journal of psychiatry. S

ao Paulo, SP.

Vol. 24, n. 4 (out. 2002), p. 189-195.

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