Prediction of Depressive Tendency from Multidimensional Health Data
Collected through Crowdsourcing
Shiori Yamaguchi1 , Hiroki Tanaka1 , Hayato Maki1 , Shigehiko Kanaya12 , Nobutaka Suzuki3 , Satoshi Nakamura12

Abstract— This paper investigates whether some aspects ofhealth data are related to depressive tendency. A limitation ofprevious studies is using only a few aspects of health data, which
motivated us to utilize a multidimensional health data. 987
participants, recruited through crowdsourcing answered 253
questions asking about their lifestyle and depressive tendency.
Classification between high and low depressive tendency wasperformed using the health data as features. A random forest
based classifier reached 0.97 accuracy, which outperformed
a logistic regression based classifier. Important features wereextracted from several categories including constitution andpersonality.

I. INTRODUCTION
Depression is one of the mood disorders, having symptoms, lost of interest or pleasure and depressed mood [1].
Many depression risk factors may exist, leading researchers to investigate the factors related to depression. Itwas suggested that the level of satisfaction regarding humanrelationship was related to depressive tendency [2]. Anotherstudy reported the strong relationship between eating alone
and depression [3].
These past studies focused on few aspects of healthdata when examining depression risk factors. In addition,
generalization ability for unknown data was not discussed inspite of creating predictive models for depressive tendency.
This is because their main interest was to investigate therelationships between depression and personal life features
as opposed to predicting depressive tendency.
The purpose of the current study is predicting depressivetendency from the multidimensional health data, and finding
more efficient questions for the purpose.
II. C OLLECTION AND ANALYSIS OF MULTIDIMENSIONALHEALTH DATA

A. Questionnaire
We created a questionnaire, which includes six questions [4] indicating depressive tendency (K6) and 247 questions about a health data, divided into ten categories (constitution, personality, demographic, communication, smoking,exercise, health condition, sleeping and dietary habits, interaction with neighbors).
1 Shiori Yamaguchi, Hiroki Tanaka, Hayato Maki are with Graduate
School of Information Science, Nara Institute of Science and Technology,
Japan. yamaguchi.shiori.yk7@is.naist.jp
2 Shigehiko Kanaya and Satoshi Nakamura are with Data Science Center,
Nara Institute of Science and Technology, Japan.
3 Nobutaka Suzuki is with Kanazawa University, Japan.

TABLE II MPORTANT QUESTIONS FROM SEVERAL CATEGORIES .
Rank
1
26
7
1326

Categories
Constitution
Constitution
Personality
Personality
Demographic
Dietary habit

Questions
Are you likely to be scared or anxious?
Do you have energy?
I am relaxed, handle stress well.
I get nervous easily.
How old are you?
How many times do you order take-away?

B. Classification
We created predictive models that classify 372 participants, recruited from crowdsourcing as high or low depressive tendency using their health data as features. The participants with K6 score 13+ were labeled as “high depressivetendency” whereas the rests were labeled as “low depressivetendency”. The participants were divided into three groups,train, validation, and test (7:1:2), and logistic regression(LR) as well as random forest (RF) were employed for theclassification.
III. R ESULTS AND C ONCLUSION
The classification accuracy of LR and RF were 0.95 and0.97, respectively. RF made one false positive and one false
negative predictions. Table I describes efficient questions
in RF, which include constitution, personality describing
neuroticism, and dietary habits.
In conclusion, this work proposed a new approach, using a
multidimensional health data and crowdsourcing. The experimental result showed the possibility that depressive tendency
is predicted by individuals’ health data.
R EFERENCES[1] B.W. Donald and G.E. Jon, DSM-5 guidebook : the essential companion to the diagnostic and statistical manual of mental disorders,
fifth edition. Washington, District of Columbia ; London, England :American Psychiatric Publishing, 2014.
[2] S.K. Davidson, C. F. Dowrick, and J. M. Gunn, Impact of functionaland structural social relationships on two year depression outcomes:
A multivariate analysis., Journal of Affective Disorders, 2016.
[3] Y. Kimura, T. Wada, K. Okumiya, Y. Ishimoto, E. Fukutomi, Y.
Kasahara, W. Chen, R. Sakamoto, M. Fujisawa, K. Otsuka, andK. Matsubayashi, Eating alone among community-dwelling Japaneseelderly: Association with depression and food diversity, Journal ofNutrition, Health and Aging, 2012.
[4] R.C. Kessler, G. Andrews, L.J. Colpe, E. Hiripi, D. K. Mroczek, S.
L. Normand, E. E. Walters, and A. M. Zaslavsky. Short screening
scales to monitor population prevalences and trends in non-specificpsychological distress., Psychological Medicine, 2002.