Abstract
OBJECTIVE: With the increasing global burden of cancer, there is a growing need for innovative strategies to improve oncology care. Health-related quality of life (HRQoL) is an outcome measure for assessing the overall wellbeing of patients with cancer. We used machine learning to predict HRQoL and to identify key factors that can inform patient-centered cancer care. METHODS: We conducted a cross-sectional study enrolling patients diagnosed with lung, breast, or colorectal cancer across two provinces in China. We collected data on demographics, clinical characteristics, and patient-centered features. HRQoL was assessed using the widely accepted EQ-5D-5L instrument in cancer care. We trained and evaluated seven machine learning models. SHapley Additive exPlanations (SHAP) analysis was employed to assess feature importance. RESULTS: Data from 924 patients with cancer were available. The random forest and extreme gradient boosting models had superior predictive performance. Positive SHAP values were primarily observed in patients with early-stage cancer and those enrolled in Urban Employees Basic Medical Insurance. Negative SHAP values were mainly associated with longer duration of chronic comorbidities, colorectal cancer, and ongoing chemotherapy. Age and time since cancer diagnosis exhibited bidirectional impacts. CONCLUSIONS: Our study demonstrates the potential of machine learning models to predict HRQoL in patients with cancer. We identified key predictors of patient HRQoL, like duration of chronic comorbidities, early-stage cancer diagnosis, age, and health insurance coverage. Our findings would facilitate early identification of patients with lower HRQoL and promote the provision of patient-centered oncology care.