A Review of the Main Factors, Computational Methods, and Databases

Used in Depression Studies

Ariane C. B. da Silva

, Renata C. Santana

, Thiago H. N. de Lima

, Maycoln L. M. Teodoro

Mark A. Song

, Luis E. Z

arate

and Cristiane N. Nobre

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

{song, zarate, nobre}@pucminas.br

Keywords:

Depression, Data Analysis, Machine Learning, Instruments.

Abstract:

Depression is a mental health disorder that affects millions of people worldwide. The disorder results from a

complex interaction of biological, psychological, and social factors, leading to difﬁculty in both prognosis and

diagnosis. In this work, we performed a review on studies about depression, to identify the main computational

techniques used to support the prediction (prognosis and diagnosis) of depression, and the main attributes that

might inﬂuence the development of the disorder. Our results indicate that, in the last ten years, Logistic

Regression, Machine Learning techniques such as Support Vector Machines and Neural Networks, and other

supervised learning algorithms, have been employed more frequently for studies predicting depression and

selecting features related to it. Attributes like insomnia, gender, marital state, and use of tobacco, for example,

were related to the development of depression. The review indicated growing effectiveness in using machine

learning methods for analyzing data related to depression.

1 INTRODUCTION

Depressive disorders are characterised by feelings of

sadness, loss of interest or pleasure, feelings of guilt

or low self-esteem, disturbed sleep or appetite, low

energy and lack of concentration. Depression can be

long lasting or recurring, substantially hindering an

individual in several environments, such as work and

school (Wray et al., 2018).

Depression results from a complex interaction of

social, psychological and biological factors. People

who have experienced adverse events in life such as

unemployment, death of loved ones or psychological

trauma are more likely to develop it (Suzuki et al.,

2018). In addition, depression can lead to more stress

and dysfunction. It worsens the life situation of the

affected person and the disorder itself.

Precisely because it involves so many factors the

diagnosis of depression is still challenging (Stapley

et al., 2016), and there is an increasing demand for

computational techniques able to provide decision

support for its diagnosis. Among these techniques, we

highlight the Machine Learning and statistical meth-

ods, widely used for health problems.

Recently, Machine Learning has achieved impres-

sive results in difﬁcult problems such as speech recog-

nition, image and video object recognition, and nat-

ural language processing (Goodfellow et al., 2016),

(Schmidhuber, 2015), (Witten et al., 2016), as well as

Bioinfomatics problems such as the prognosis of dis-

eases and disorders such as depression (Khademi and

Nedialkov, 2015), (Rav

ı et al., 2017) . In 2017, the

CLEF eRisk, an international workshop brought to-

gether works aiming at the early prediction of risks on

the internet, including people with suicidal inclination

or people susceptible to depression. Machine learn-

ing techniques such as Random Forest (RF), Naive

Bayes (Villegas et al., 2017), Recurrent Neural Net-

works (RNN) (Sadeque et al., 2017), Support Vec-

tor Machine (SVM) and Ensemble methods (Trotzek

et al., 2017), (Almeida et al., 2017) were used to pre-

dict as early as possible the risk of mental health is-

sues from user-generated content in social media.

Techniques capable of predicting, with high accu-

racy, whether a person may develop depression are

important to aid in its diagnosis and, consequently, its

prognosis.

B. da Silva, A., Santana, R., N. de Lima, T., Teodoro, M., Song, M., Zárate, L. and Nobre, C.

A Review of the Main Factors, Computational Methods, and Databases Used in Depression Studies.

DOI: 10.5220/0010815800003123

In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 5: HEALTHINF, pages 413-420

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

413

A review of studies carried out in this area could

support research focused on computational tech-

niques and methods of computer science and statis-

tics, applicable to the problem of predicting whether

a person might develop depression and characterizing

its proﬁle. This review structures relevant works that

answer questions relevant to the area, assisting in the

investigation and clinical treatment of this complex

mental disorder.

In order to identify the main works that employed

computational approaches in the diagnosis of people

with depression, three research questions, presented

in Section 2, guided this study. The questions fo-

cus on the following: 1) the main characteristics re-

lated to people with depression, 2) the main compu-

tational methods used in its analysis and 3) what kind

of databases are being used in this context .

The following Sections describe the protocol used

in this review article, and the answers we found for

the research questions we proposed.

2 METHOD

First, the research questions were speciﬁed, followed

by the development and validation of a research pro-

tocol including the inclusion/exclusion criteria for the

selection of documents. After that, the relevant ar-

ticles were selected based on the criteria adopted in

the previous phase, and have their qualities assessed,

and results summarised. Finally, in the last phase, the

results of the review were analysed and made into a

report that is then validated.

Subsections 2.1 and 2.2 better detail the process

adopted.

2.1 Research Questions

Considering that the formulation of research ques-

tions should be guided by their signiﬁcance and

importance for professionals and researchers in the

ﬁeld, three research questions (RQs) were formulated

for this study:

RQ 1. What are the main factors related to depression

that are cited in the literature?

RQ 2. Which computational methods, related to ma-

chine learning and statistics, are used in depression

studies?

RQ 3. What are the databases available for depres-

sion analyses?

2.2 Considered Repositories

Our choice of data sources included the main reposi-

tories of computer science articles, such as the Asso-

ciation for Computing Machinery (ACM), the Insti-

tute of Electrical and Electronics Engineers (IEEE),

ScienceDirect, and repositories related to health stud-

ies, such as PubMed. These repositories were chosen

for their scope, as they involve areas related to health

(biology, psychology, and specialised medicine top-

ics) and computing. It is relevant to highlight that,

at least, one of the terms “depression” or “depressive

disorder” needed to be present in the title or abstract

of the document and one of the terms “machine learn-

ing”, “logistic regression”, “data mining” or “statisti-

cal analysis” could be present anywhere in the text.

The process of eliminating articles was applied

with the following criteria: 1) Articles written in lan-

guages other than English and those that were not

directly related to at least two of the research ques-

tions were excluded; 2) Articles reporting on the same

study were considered equivalent, and the most re-

cent version was kept; 3) Books, tutorials, editori-

als, posters, panels, transcripts of lectures, workshops

and demonstration materials have been disregarded;

4) Articles dealing with pharmacological treatment

and analysis of its effects were disregarded, as they

are not in the scope of this review.

Table 1 presents the searched digital repositories

and the number of articles kept in each selection

phase. Each article returned by the searches was re-

viewed individually by the authors of this paper, and

the decision to keep or discard a subset of an article

was made based on a majority vote.

Table 1: Number of articles kept after each of the three ﬁl-

tering phases of the review.

Datasets Initial Filter Filter Filter

Filter by by by

title abstract reading

ACM 39 25 23 12

IEEE 17 14 12 11

PubMed 493 233 68 26

ScienceDirect 232 83 33 7

Total 781 355 136 56

There were 56 articles

, identiﬁed with the letter

“A” followed by a number from 1 to 56, and then an

indication of the repository from where the article was

obtained and its publication year.

Data from the all articles selected after the entire selec-

tion/exclusion phase can be accessed at https://drive.google.

com/ﬁle/d/1G7EVcdiVqpgoghW1Go3E314lmuIaYOXg/

view?usp=sharing.

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414

3 DISCUSSION

The research questions presented in subsection 2.1 are

discussed in this Section and related to data and infor-

mation covered in the selected articles of this review.

3.1 What Are the Main Factors Related

to Depression Cited in the

Literature?

Depression is a common illness that negatively affects

people’s quality of life. Its diagnosis is difﬁcult as

depression is caused by the interaction of varied fac-

tors such as biological, psychological, and social (Pa-

tel et al., 2007), among others, making it easy to be

misdiagnosed as different disorders, or not at all.

In order to identify these main causes, we propose

a taxonomy of relevant factors related to depression

that was obtained by integral reading the articles se-

lected in this review.

The taxonomy is presented in Figure 1, and con-

sists of four broad dimensions: 1) Comorbidities, 2)

Risk factors, 3) Identiﬁed phases of depression, and

4) Main symptoms. In addition, each of these four

broad dimensions was subdivided into more speciﬁc

aspects at different levels of granularity.

Observing Figure 1, the complexity of the interac-

tion between the factors that characterize depression

is clear, either by the number of factors or by the sub-

divisions that cover distinct but related areas: behav-

ioral, neurobiological or health-related, geographical,

socioeconomic and demographic factors. Apparently

unrelated factors such as smoking, number of people

in the family, and place of residence may, together or

not, trigger the process leading to depression. The

taxonomy highlights the difﬁculty inherent in the task

of diagnosing and predicting depression.

There is a connection between risk factors, co-

morbidities, major symptoms, and age/physiological

phases, concerning depression (Figure 1). We also

noted a prevalence of research on adults and people

with chronic diseases, to the detriment of other age

groups.

Risk factors can be subdivided into six main sub-

groups: behavioral, psychological, demographic, ge-

ographic, socioeconomic, neurobiological, and gen-

eral health. Regarding demographic and socioeco-

nomic questions, there is a predominance of questions

regarding age, sex and education. Such factors may

directly inﬂuence the diagnosis of the disorder since,

for example, it is known that women tend to be di-

agnosed with depression more often than men (Beck

and Alford, 2011) (Silva et al., 2014).

Regarding the most frequent symptoms in depres-

sion, sleep disorders, depressed mood, and fatigue

were the most recurrent in the studies evaluated. Such

symptoms can be easily stigmatized, that is, people

who live with a depressed individual can mistake their

symptoms with laziness and lack of interest and, con-

sequently, blame the patient. This kind of stigma has

its roots in the very construction of the deﬁnition of

the term “depression” - which, for centuries, was not

understood as a disorder that depended on the proper

care of medicine, but rather as something related to

spirituality (Quevedo et al., 2018).

Knowing and understanding the broad intercon-

nected aspects presented by this taxonomy helps in

holistically understanding depression. Treatments

that disregard the scope of the disorder may be

doomed to fail. Therefore, the challenge is to act on

different levels to enhance the quality of life of people

with depression.

3.2 Which Computational Methods,

Related to Statistics and Machine

Learning, Are Used in Depression

Studies?

Most of the works analyzed in this study used ei-

ther Logistic Regression, Neural Networks or Support

Vector Machines (SVM).

Patel et al. (2016) present a research from previ-

ous work that focuses on the study of depression and

uses features related to magnetic resonance imaging

to generate prediction models for depression. Four-

teen papers, of the ﬁfteen selected by the authors, used

Support Vector Machines (SVM).

Galiatsatos et al. (2015) employed Bayesian Net-

works to create an optimised model of psychologi-

cal factors that lead study patients to have thoughts

of death or suicide in order to more quickly achieve

appropriate treatment. According to the authors, the

results of these experiments ratify the opinion of some

experts that the most signiﬁcant factors affecting men-

tally depressed patients, who have thoughts of death

or suicide, are mood swings, loss of interest or plea-

sure, feelings of indignity/guilt, living in urban areas

and low focus.

Yang et al. (2016) used decision tree algorithms

to classify people with depression. The decision tree

was built according to the PHQ-8 score (Kroenke

et al., 2009) and other characteristics of the partici-

pants, obtained through the analysis of transcription

ﬁles from their consultations. Their model obtained

results, reaching a F1-score of 0.571 for the depressed

class and 0.877 for the non-depressed class.

A Review of the Main Factors, Computational Methods, and Databases Used in Depression Studies

415

Figure 1: Factors related to depression.

Patel et al. (2015) used various machine learn-

ing methods with multimodal image input to cre-

ate prediction models for the diagnosis of depres-

sion in the elderly or late depression and treatment

response. Their demographic and cognitive ability

data were recorded, and brain characteristics were ac-

quired using multimodal magnetic resonance imaging

pre-treatment. Linear and nonlinear learning methods

were tested, and decision trees obtained the most ac-

curate prediction models for the diagnosis of late de-

pression (87.27% accuracy) and treatment response

(89.47% accuracy). The diagnostic model included

age measures, mental state examination scores and

structural imaging. The authors conclude that com-

binations of images and measurements might help to

better predict a diagnosis of late depression and treat-

ment response. They further add that these ﬁndings

may help better understanding of late depression and

identify preliminary steps for personalised treatment

of late depression.

Tung and Lu (2016) and Chen et al. (2020) used

statistical models applied to text mining to analyze

and predict signs or terms of depression in web posts.

The authors propose their own extraction method to

automatically detect negative event terms. The ﬁrst

article proposed model gets a recall and F-measure

of 0.668 and 0.624, respectively and the authors con-

clude that the developed model can help doctors more

effectively diagnose and even track the depression of

web post authors.

Tadesse et al. (2019) also investigated texts posted

on social media combining machine learning and Nat-

HEALTHINF 2022 - 15th International Conference on Health Informatics

416

ural Language Processing (NLP) techniques, seeking

to identify a set of more common terms in posts from

users with depression. The authors concluded that

with the use of an especiﬁc NLP technique, the clas-

siﬁer with the best performance is the SVM with 80%

accuracy and 0.80 F1 scores, comparing to combined

NLP techniques associated to Multilayer Perceptron

(MLP) neural network that reached 91% accuracy and

0.93 F1 scores.

Yanikkerem et al. (2013) employed the Chi-

squared test and Fisher’s test to identify the rela-

tionship between social and demographic variables

related to pregnant women and their average scores

of a self-evaluation questionnaire that determines the

severity of depression. The authors identiﬁed that low

schooling levels, unplanned pregnancies, lack of so-

cial support, and pregnancy-related physical symp-

toms were the most important risk factors for deter-

mining prenatal depression.

Sengupta and Benjamin (2015) constructed a lo-

gistic regression model to estimate the prevalence of

depression and identify risk factors associated with

depression on the elderly population. The authors

identiﬁed that residing in urban areas, being female,

old age, living alone, poverty and functional and cog-

nitive impairment are strong predictors for depres-

sion.

Some recent studies have been using the combina-

tion of classiﬁers in order to further improve the pre-

diction performance of their models. Yang and Bath

(2019) proposed a system framework, to predict de-

pression in older age and assess a large number of

factors from different domain areas associated with

it, combining Gradient Boosting Machine (GBM),

Keras-based Convolutional Neural Network (KNN),

Regularized Greedy Forest (RGF) and Logistic Re-

gression. Through the proposed model, the authors

identiﬁed and separated the factors related to depres-

sion by domain area, aiming at the development of

appropriate help strategies.

Mahendran et al. (2020) proposed a stacking

based ensemble learning model to improve the predic-

tion accuracy of major depressive disorder in adults.

This model uses KNN for preprocessing, Random

Forest for feature selection, MLP, SVM and Random

Forest as low-level learners. The authors conﬁrmed

that the prediction accuracy of the combined model is

98.16% against 96.9% in the best result of an isolated

classiﬁer.

3.3 What Are the Datasets Available for

Depression Analyses?

Regarding the databases used in the studies, several

articles use in their experiments data obtained from

medical records of patients in clinics and hospitals

(Matza et al., 2011), (Koleva et al., 2011), (Min et al.,

2012), (Yanikkerem et al., 2013), (Hosseinifard et al.,

2013), (Zeng et al., 2014), (de Moraes et al., 2016),

(Xing et al., 2019), (Wan et al., 2020), (Mahendran

et al., 2020), however none of this data is publicly

available.

Wang et al. (2019) used the Japanese Female Fa-

cial Expression Database (JAFFE), an image dataset

developed at Kyushu University, Japan. It consists

of 10 Japanese female expressers, 7 posed facial ex-

pressions (6 basic facial expressions + 1 neutral) be-

ing several images of each expression for each ex-

presser, totaling 213 images. Each image has aver-

aged semantic ratings on 6 facial expressions by 60

Japanese viewers with resolution 256x256 pixels and

8-bit grayscale. The JAFFE images may be used for

all kinds of non-commercial scientiﬁc research.

In (Sunmoo et al., 2014), the dataset used was

The Behavioral Risk Factor Surveillance System

(BRFSS), a US health research system with data col-

lected via telephone calls. The purpose of the BRFSS

is to collect speciﬁc data on health risk behaviors,

diseases and chronic conditions, access to health ser-

vices, and use of preventive health services related

to the leading causes of death and disability in the

United States. The latest BRFSS data, from 2017,

contains 450,016 records and 358 attributes. Fac-

tors assessed by the BRFSS in 2017 included health

status, healthy days, quality of life, access to health

services, physical exercise, inadequate sleep, chronic

health conditions, oral health, smoking, electronic

cigarettes, alcohol consumption, immunization, falls,

seat-belt use, knowledge about HIV/AIDS, among

others.

In (Yang et al., 2016), the authors utilised data

from the Distress Analysis Interview Corpus - Wiz-

ard of Oz (DAIC-WOZ) database, where clinical in-

terviews are published to be used to support the diag-

nosis of psychological disorders such as anxiety and

depression. Collected data includes audio and video

recordings and questionnaire responses in which a

variety of verbal and nonverbal characteristics were

transcribed and recorded. The database includes 189

interaction sessions ranging from 7-33 minutes (av-

eraging 16 minutes). Each session includes the tran-

script of the interaction, participant audio ﬁles and fa-

cial features.

Dipnall et al. (2016) used data from the Na-

A Review of the Main Factors, Computational Methods, and Databases Used in Depression Studies

417

Table 2: Addresses for accessing the datasets.

Abbreviation

Location Sample Size

Base Year URL

JAFFE Japan 213 1998 https://zenodo.org/record/3451524#.YVt8DDHMKiM

BRFSS United States 450016 2017 https://www.cdc.gov/brfss/data documentation/index.htm

DAIC-WOZ United States 189 2017 http://dcapswoz.ict.usc.edu

NHANES United States 9544 2015-2016 https://www.cdc.gov/nchs/nhanes/index.htm

KNHANES South Korea 8518 2011 https://knhanes.cdc.go.kr/knhanes/intro.html

NPHS Canada 17276 2010-2011 https://www.statcan.gc.ca/eng/survey/household/3225

tional Health and Nutrition Examination Survey

(NHANES), a cross-sectional study program that

combines interviews and physical examinations to as-

sess the health and nutritional status of adults and

children in the United States. In the article, the au-

thors identiﬁed three bio-markers associated with de-

pression, from an initial set of 67: 1) red blood cell

distribution, 2) serum glucose, and 3) total biliru-

bin. The association between depression and the three

bio-markers is widely cited in the literature (Maes

et al., 2011) (Miller et al., 2009) (Stewart et al., 2009)

(Winokur et al., 1988).

In (Chung et al., 2014), the authors used a Ko-

rean version of the same study, the Korea Na-

tional Health and Nutrition Examination Survey (KN-

HANES). KNHANES is a cross-sectional study con-

ducted by South Korea’s National Health and Welfare

Division and is made up of three different sections: 1)

a health interview, 2) a health check and 3) a nutrition

survey. Psychological health data were obtained from

a self-reported mental health questionnaire under the

supervision of an investigator.

Wang et al. (2013) performed analyzes based on

data provided by the National Population Health Sur-

vey (NPHS). The NPHS is a longitudinal survey con-

ducted every two years in Canada since 1994/1995.

The ﬁrst three research cycles (1994/1995, 1996/1997

and 1998/1999) were cross-sectional and longitudi-

nal. Starting on cycle 4 (2000/2001), it became

strictly longitudinal, that is, collecting health infor-

mation and factors that may inﬂuence it from the same

individuals in each cycle (Catlin and Will, 1992). The

NPHS longitudinal sample consists of 17,276 people

and it was not renewed over time.

Table 2 presents information that characterizes the

mentioned datasets, including the web address of each

database, for more information. Each data set is iden-

tiﬁed in the Table by its acronym.

4 CONCLUSIONS

In this work, we sought to answer three research ques-

tions through a review of the literature. We created

a taxonomy of factors related to depression, relat-

ing to the ﬁrst question. The taxonomy connects

the risk factors, comorbidities, main symptoms and

age/physiological phases investigated in relation to

depression. There is a prevalence of research on

adults and/or people with chronic diseases. Regarding

demographic and socioeconomic questions, there is a

predominance of questions regarding age, sex and ed-

ucation. Six subgroups of risk factors were identiﬁed:

behavioral, psychological, demographic, geographic,

socioeconomic, neurobiological and health.

Analysis of the most recent studies of machine

learning techniques in the context of depression has

signaled a tendency to combine several classiﬁers to

improve the performance of the model, and ensemble

methods have been increasingly used.

Regarding the databases available for depression

investigation, it was observed that few studies made

the datasets they used publicly available, which does

not encourage further studies. We also note that the

NPHS longitudinal database signals an interest of the

scientiﬁc community to evaluate users over time.

It is necessary to consider the limitation of the

used method, as such established questions and pro-

tocols are affected by the subjectivity of the re-

searcher(s) who perform it. The researchers need

to make article/content selections to facilitate the re-

search in a timely manner throughout the review pro-

cess. In our study, the evaluation of the articles had

the participation of all authors, who together decided

whether or not the article should remain in the review.

This helped to reduce the subjectivity in the data se-

lection phase.

We also considered that if the searches had been

performed in other database repositories with differ-

ent strings, we could have found references to other

computational methods and techniques, such as deep

learning. For this reason, we understand that this is

also a limitation since the terms and journals chosen

to mark the search space and, consequently, the re-

turned articles. Although this limitation exists, clarity

about this is necessary to detect possible failures and

also further work in the future.

In this article, we show a compilation of the main

contributions of research analyzing the depression

disorder, and show how this mental disorder has been

investigated from a computing perspective. In this

HEALTHINF 2022 - 15th International Conference on Health Informatics

418

context, statistical methods can be applied in con-

junction with different machine learning techniques

at different stages of knowledge discovery processes

in order to produce increasingly assertive models in

the selection of relevant characteristics and in the ﬁ-

nal prediction of depression and related disorders.

ACKNOWLEDGEMENTS

The authors thank the National Council for Scientiﬁc

and Technological Development of Brazil (CNPq),

the Coordination for the Improvement of Higher Ed-

ucation Personnel - Brazil (CAPES), the Founda-

tion for Research Support of Minas Gerais State

(FAPEMIG) and Pontiﬁcal Catholic University of Mi-

nas Gerais (PUC Minas).

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