Relationship matters: Using machine learning methods to predict the mental health severity of Chinese college freshmen during the pandemic period

Abstract

BackgroundPandemics act as stressors and may lead to frequent mental health disorders. College student, especially freshmen, are particularly susceptible to experiencing intense mental stress reactions during a pandemic. We aimed to identify stable and intervenable variables including academic, relationship and economic factors, and focused on their impact on mental health severity during the pandemic period. MethodsWe innovatively combined diverse machine learning methods, including XGBoost, SHAP, and K-means clustering, to predict the mental health severity of college freshmen. A total of 3281 college freshmen participated in the research. Discriminant analyses were performed on groups of participants with depression (PHQ-9), anxiety (GAD-7). All characteristic variables were selected based on their importance and interventionability. Further analyses were conducted with selected features to determine the optimal variable combination. ResultsXGBoost analysis revealed that relationship factors exhibited the highest predictive capacity for mental health severity among college freshmen (SHAPFamily Relationship = 0.373; SHAPPeer Support = 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 among different subgroups. ConclusionsThe proposed machine learning approach utilizing XGBoost, SHAP and K-means clustering methods provides a valuable tool to gain insights into the relative contributions of academic, relationship and economic factors to Chinese college freshmen's mental health severity during the COVID-19 pandemic. The result guide the development of targeted intervention measures tailored to meet specific requirements within each subgroup.