首页|Tsinghua University Reports Findings in Mental Health Diseases and Conditions (R elationship matters: Using machine learning methods to predict the mental health severity of Chinese college freshmen during the pandemic period)
Tsinghua University Reports Findings in Mental Health Diseases and Conditions (R elationship matters: Using machine learning methods to predict the mental health severity of Chinese college freshmen during the pandemic period)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Mental Health Diseases and Conditions is the subject of a report. According to news reporting out of B eijing, People’s Republic of China, by NewsRx editors, research stated, “Pandemi cs act as stressors and may lead to frequent mental health disorders. College st udent, especially freshmen, are particularly susceptible to experiencing intense mental stress reactions during a pandemic.” Our news journalists obtained a quote from the research from Tsinghua University , “We aimed to identify stable and intervenable variables including academic, re lationship and economic factors, and focused on their impact on mental health se verity during the pandemic period. We innovatively combined diverse machine lear ning methods, including XGBoost, SHAP, and K-means clustering, to predict the me ntal health severity of college freshmen. A total of 3281 college freshmen parti cipated in the research. Discriminant analyses were performed on groups of parti cipants with depression (PHQ-9), anxiety (GAD- 7). All characteristic variables w ere selected based on their importance and interventionability. Further analyses were conducted with selected features to determine the optimal variable combina tion. XGBoost analysis revealed that relationship factors exhibited the highest predictive capacity for mental health severity among college freshmen (SHAP = 0. 373; SHAP = 0.236). The impact of academic factors on college freshmen’s mental health severity depended on their intricate interplay with relationship factors, resulting in complex interactive effects. These effects were heterogeneous amon g different subgroups. The proposed machine learning approach utilizing XGBoost, SHAP and K-means clustering methods provides a valuable tool to gain insights i nto the relative contributions of academic, relationship and economic factors to Chinese college freshmen’s mental health severity during the COVID-19 pandemic. ”
BeijingPeople’s Republic of ChinaAsi aCyborgsEmerging TechnologiesEpidemiologyHealth and MedicineMachine Le arningMental Health Diseases and ConditionsPandemic
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