[2J3] [General Session] 13. AI Application

[2J3-02] Estimation of Depressive Tendency Based on Lifestyle and Constitution

〇Shiori Yamaguchi¹, Hiroki Tanaka¹, Hayato Maki¹, Shigehiko Kanaya ^1,2, Nobutaka Suzuki³, Satoshi Nakamura ^1,2 (1. Nara Institute of Science and Technology, Graduate School of Science and Technology, 2. Nara Institute of Science and Technology, Data Science Center, 3. Kanazawa University, Graduate School of Medical Sciences)

Keywords:depression , crowdsourcing , health care , lifestyle, machine learning

This paper investigates the possibility of predicting depressive tendency by lifestyle data. Previous studies analyzed few aspects of lifestyle, whereas the current study utilizes multivariate analysis to examine multiple perspectives of lifestyle, such as social, sleeping, and dietary habits. We created a questionnaire including depressive tendency score (K6) as well as lifestyle, and recruited 987 participants, who answered it using a crowdsourcing service. Classification models were obtained using machine learning to classify the participants as high or low depressive tendency. Random forest classifier achieved 0.97 accuracy. Particularly effective features were chosen from Chinese medicine, and personality describing neuroticism.