Design of Depression Assessment and Early Warning System for College Students based on Machine Learning and Daily Health Data

被引：0
|
作者：
Chen, Xin [1 ,2 ,3 ]
Xu, Liangwen [2 ]
Pan, Zhigeng [1 ,2 ,3 ,4 ]
机构：
[1] Hangzhou Normal Univ, Inst VR & Intelligent Syst, Hangzhou 311121, Peoples R China
[2] Hangzhou Normal Univ, Sch Med, Hangzhou 311121, Peoples R China
[3] Hangzhou Normal Univ, Engn Res Ctr Mobile Hlth Management Syst, Minist Educ, Hangzhou 311121, Peoples R China
[4] Nanjing Univ Informat Sci & Technol, Sch Artificial Intelligence, Nanjing 210044, Peoples R China
来源：
THIRTEENTH INTERNATIONAL CONFERENCE ON GRAPHICS AND IMAGE PROCESSING (ICGIP 2021) | 2022年 / 12083卷
关键词：
machine learning; health data; college students; depression; rapid assessment; early health warning;
D O I：
10.1117/12.2623546
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
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.
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页数：5
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