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Prediction of comorbid anxiety and depression using machine learning models in cancer survivors


 Background

We aimed to investigate the prevalence and associated factors of comorbid anxiety and depression (CAD) in Chinese cancer survivors (CS), and develop novel prediction model using traditional logistic regression and machine-learning algorithms to predict CS’ CAD, which would be helpful to accurately distinguish individuals with high risk of psychological problems.
Methods

A cross-sectional study of 1546 CSs were conducted in Shanghai China. Self-reported structured questionnaire was used to collect information about basic socio-demographic factors, socioeconomic status, life behavior, health conditions, fatigue, social support, anxiety (Zung self-rating anxiety scale) and depression (Zung self - rating depression scale). Stepwise logistic regression and three machine learning algorithms (support vector machine, decision tree, and random forest) were used to construct the prediction model of CS’ CAD.
Results

18.24% CSs reported CAD. The AUC of models to predict the high-risk CAD in the training set was best in that of RF (0.839), followed by LR (0.811), SVM (0.808), and DT (0.794). Social support and several comorbid chronic diseases were important top ranking contributing factors of CAD in all the predictive models.
Conclusion

CAD was a common psychological problems in cancer survivors (CSs).The machine leaning techniques and logistic regression showed similar performance to classify Chinese CSs at risk of CAD using questionnaire-based survey. Effective treatment to control the coexisting disease, more social support should be provided to CSs to improve psychological health. Psychological care was recommend to be incorporated into regular health care and management of cancer survivors.