Design of depression assessment and early warning system for college students based on machine learning and daily health data

Xin Chen; Liangwen Xu; Zhigeng Pan

doi:10.1117/12.2623546

16 February 2022 Design of depression assessment and early warning system for college students based on machine learning and daily health data

Xin Chen, Liangwen Xu, Zhigeng Pan

Author Affiliations +

Proceedings Volume 12083, Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021); 1208308 (2022) https://doi.org/10.1117/12.2623546
Event: Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021), 2021, Kunming, China

Abstract

In China, college students have a high incidence of depression. The assessment and early warning of depression are conducive to the management of colleges and universities and the development of college students. In this paper, we designed a rapid assessment and early warning system of college students' depression based on sensors and daily health data. Logistic Regression Model and Multilayer Perceptron Model were used in the system. The system has the function of depression early warning, which can provide students with timely psychological treatment and intervention in the early stage of depression, so as to prevent the tragedy caused by mental health problems. Through the simple sensor data and health data collected daily, the system can make rapid judgment.

Citation Download Citation

Xin Chen, Liangwen Xu, and Zhigeng Pan "Design of depression assessment and early warning system for college students based on machine learning and daily health data", Proc. SPIE 12083, Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021), 1208308 (16 February 2022); https://doi.org/10.1117/12.2623546

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