Identifying the predictors of severe psychological distress by auto-machine learning methods

Social stress in daily life and the COVID-19 pandemic have greatly impacted the mental health of the population. Early detection of a predisposition to severe psychological distress is essential for timely interventions. This paper analyzed 4036 samples participating in the 2019-2020 National Health Information Trends Survey (HINTS) and identified 57 candidate predictors of severe psychological distress based on univariate chi-square and t-test analyses. Five machine learning methods, namely logistic regression (LR), automatic generalized linear models (Auto-GLM), automatic random forests (Auto-Random Forests), automatic deep neural networks (Auto-Deep learning) and automatic gradient boosting machines (Auto-GBM), were employed to model synthetic minority oversampling technique-based (SMOTE) resampled data and identify predictors of severe psychological distress. Predictors were evaluated by odds ratios in logistic models and variable importance in the other models. Forty-seven variables were identified as significant predictors of severe psychological distress, including 13 sociodemographic variables and 34 variables related to individual lifestyle and behavioral habits. Among them, new potentially relevant variables related to an individual's level of concern and trust in cancer information, exposure to health care providers, and cancer screening and awareness are included. The performance of each model was evaluated using five-fold cross-validation. The optimal model performance-wise was Auto-GBM with an accuracy of 89.75%, a precision of 89.68%, a recall of 89.31%, an F1-score of 89.48% and an AUC of 95.57%. Significant predictors of severe psychological distress were identified in this study and the value of machine learning methods in predicting severe psychological distress is demonstrated, thereby enhancing pre-prediction and clinical decision-making of severe psychological distress problems.