What question did this study set out to answer?

This study aims to identify predictors of anxiety and effective classifiers, examining how demographic and psychosocial factors influence anxiety risk among cancer patients.

July 19, 2026Open Access

A machine learning approach to profiling anxiety risk among cancer patients on treatment

KYKailei YanUniversity of Central Florida MSM Kabiruzzaman ShahUniversity of Central Florida HWHsiao-Lan WangUniversity of Alabama at Birmingham

Key Points

This study aims to identify predictors of anxiety and effective classifiers, examining how demographic and psychosocial factors influence anxiety risk among cancer patients.
Utilized machine learning models including regularized regressions and classifiers like SVM, random forest, and XGBoost.
Evaluated predictors using multiple imputed datasets and various performance metrics.
Applied SHapley Additive exPlanations (SHAP) to enhance model interpretability.
Elastic net showed the lowest average prediction error (MSE = 65.486).
Random forest achieved the highest mean accuracy (0.751), while logistic regression achieved the highest ROC-AUC (0.835).
Key predictors include participation self-efficacy and various psychosocial and demographic risk combinations.

Abstract

PURPOSE: Previous literature has identified multiple risk factors for anxiety among individuals with cancer. However, the relative importance across many interrelated variables is not clear. Further, it is unknown how combined psychosocial factors and demographic/clinical characteristics increase an individual's vulnerability to anxiety. The aims of this study are to systematically test the following: (1) the best predictors of anxiety, (2) the best-performing classifiers for distinguishing high versus low anxiety, and (3) the most informative combinations of psychosocial features and demographic/clinical characteristics that heighten an individual's vulnerability to anxiety. METHODS: Machine learning (ML) models were used. For Aim 1, we tested the predictors of anxiety using the models of regularized regressions, GAMS, and best subset selection. For Aim 2, models tested included support vector machines (SVM), random forest, nearest neighbors, and XGBoost as the classifiers to predict high versus low anxiety. For Aim 3, gradient-boosted decision tree models were trained to examine the most important combinations of candidate predictors. We used SHapley Additive exPlanations (SHAP) to improve the interpretability of our ML models. RESULTS: Across five imputed datasets, using linear regression as the primary benchmark (MSE = 69.945 ± 8.131; MAE = 6.636 ± 0.361), elastic net showed the lowest average prediction error (MSE = 65.486 ± 8.323; MAE = 6.504 ± 0.357), although differences among predictor-based models were modest. For classifiers, random forest achieved the highest mean accuracy (0.751 ± 0.015), kNN achieved the highest PR-AUC (0.797 ± 0.016), and logistic regression achieved the highest ROC-AUC (0.835 ± 0.005). Participation self-efficacy was the most consistent key predictor of anxiety across models. The most important risk combinations of demographic and psychosocial/symptom variables included age-symptom communication barriers, age-cancer coping self-efficacy, ECOG performance status-depressive symptoms, the type of helper-psychological well-being, and education-psychological well-being. CONCLUSION: Our findings may inform risk stratification in clinical settings and facilitate personalized intervention designs aiming at relieving anxiety among cancer patients.

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