[3Xin4] Poster session 1

[3Xin4-80] Classification of Mental Disease by Machine Learning and study of Effective Features

〇Hiroya Tanaka¹, Sho Katsuki², Hironobu Nakamura³, Taishiro Kishimoto⁴, Yoshinobu Kano² (1.Natural Institute of Technology Kurume College, 2.Shizuoka University, 3.Tokyo Medical and Dental University, 4.Keio University)

Keywords:Mental Health, Disease Type Prediction

We have been creating our UNDERPIN mental disease dialogue corpus, which includes more than 1000 hours of recorded voice. Our corpus consists of patients' information (disease name, drugs, etc.), disease test results, and dialogue data. We classified the disease types (bipolar disorder, schizophrenia, dementia, depression, anxiety) versus healthy people, using audio and linguistic features extracted from the corpus. We achieved around 75-91 points in f-score depending on the disease types, which feature importance suggested that formants, fillers, laughs, and questions are important indicators to predict mental diseases.