Using Voice Data to Facilitate Depression Risk Assessment in Primary Health Care
Conference
Goyal, A, Ho Chun Man, R, Lee, RKW et al. (2024). Using Voice Data to Facilitate Depression Risk Assessment in Primary Health Care
. 17-18. 10.1145/3630744.3658408
Goyal, A, Ho Chun Man, R, Lee, RKW et al. (2024). Using Voice Data to Facilitate Depression Risk Assessment in Primary Health Care
. 17-18. 10.1145/3630744.3658408
Goyal, A; Ho Chun Man, R; Lee, RKW; Saha, K; L. Altice, F; Poellabauer, C; Papakyriakopoulos, O; Yin Cheung, L; De Choudhury, M; Allagh, K; Kumar, N
abstract
Voice-only telehealth is often more practical for lower-income patients who may lack stable internet connections. Thus, our study focused on using voice data to predict depression risk. The objectives were to: 1) Collect voice data from 24 people (12 with depression and 12 without mental health or major health condition diagnoses); 2) Build a machine learning model to predict depression risk. TPOT, an autoML tool, was used to select the best machine learning algorithm, which was the K-nearest neighbors classifier. The selected model had high performance in classifying depression risk (Precision: 0.98, Recall: 0.93, F1-Score: 0.96), compared to previous models. These findings may lead to a range of tools to help screen for and treat depression.
