Prediction of Self-management Strategies for Future Quality of life and Health Statuses in Cancer Patients Using Machine Learning Techniques: A Prospective Cohort Study with a Web-Based Survey (Preprint)
Authors: Jung Ju Youn , Yun Young Ho 
Published: August 2022

BACKGROUND

Despite the importance of improving the quality of life (QoL) and overall health status (physical, mental, social, and spiritual health status) of cancer patients and survivors, the prediction model for QoL and health status and the interpretation of the model are insufficient. As self-management strategies were essential for improving the QoL and health status in cancer patients, we tried to develop simple prediction models using only self-management strategies and personal characteristics such as sociodemographic and clinical variables.

OBJECTIVE

First, we aimed to develop and validate simple prediction models for QoL as a primary outcome and health statuses as secondary outcomes considering the use in actual clinical practice. Second, we aimed to interpret the prediction models using explainable artificial intelligence (XAI) methods by extracting important features and dependence plots. Third, we aimed to predict and interpret individual outcomes and visualize the results using the XAI method called SHapley Additive explanation (SHAP). This study's ultimate goal was to provide customized healthcare services for cancer patients and survivors to improve their QoL and health statuses.

METHODS

In this prospective cohort study with a web-based survey, we established the prediction model for QoL and health statuses and compared the performance with ensemble methods such as decision trees, random forest, gradient boosting, eXtreme Gradient Boost (XGBoost), and LightGBM. After the model comparison, we selected the XGBoost model for further analysis. We demonstrated critical features of the QoL and each health status separately and extracted the individual prediction result in the XGBoost model using SHAP.

RESULTS

After data preprocessing and model selection, the final data set contained 256 cancer patients with 38 predictive features. On repeated stratified K-fold validation, the XGBoost predictive model for the QoL showed high performance: area under the receiver operating characteristic curve (AUROC) of 0.80 (95% CI, 0.78-0.82) and area under the precision-recall curve (AUPRC) of 0.96 (95% CI, 0.95-0.97). The performance of the XGBoost predictive model for each health status was 0.66 (95% CI, 0.65-0.67) for AUROC and 0.77 (95% CI, 0.75-0.79) for AUPRC in predicting physical health status, 0.71 (95% CI, 0.69-0.73) for AUROC and 0.90 (95% CI, 0.89-0.92) for AUPRC in predicting mental health status, 0.77 (95% CI, 0. 75-0.79) for AUROC and 0.96 (95% CI, 0.95-0.97) for AUPRC in predicting social health status, 0.75 (95% CI, 0.74-0.76) for AUROC and 0.87 (95% CI, 0.85-0.89) for AUPRC in predicting spiritual health status. The important features of the above predictive models were different. The top three important features of the QoL predictive model were the activity coping strategy, the change values of the self-implementing strategy, and the proactive problem-solving strategy, which were all self-management strategies for cancer patients. Our study also showed a specific method of providing personalized healthcare services.

CONCLUSIONS

To our knowledge, this study was the first to develop and validate interpretable predictive models for the QoL and health status in cancer patients and suggest the potential of providing customized healthcare with a specific application for cancer patients using the XAI technology.