ArticleBreast cancer (Tokyo, Japan)2026
Interpretable machine-learning survival prediction of breast cancer prognosis from lifestyle factors: evidence from UK biobank.
Article in Breast cancer (Tokyo, Japan), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundUnderstanding how lifestyle habits influence breast cancer survival is crucial for improving long-term outcomes and guiding individualized care. The purpose of this study is to comprehensively understand how dietary habits, exercise frequency, sleep quality and the frequency of tobacco and alcohol use affect the survival of breast cancer patients by analyzing the synergistic effects of multiple lifestyle habits.
methodsWe analyzed data from 21,219 female breast cancer patients in the UK Biobank, a large-scale population-based cohort. Three feature selection methods were applied to identify key prognostic variables. Five survival risk models were compared to investigate the relationship between breast cancer survival and lifestyle habits. Kaplan-Meier survival analysis and SHapley Additive exPlanations (SHAP) interpretability analysis were performed on risk scores calculated from the best-performing model.
resultsThrough feature selection, we identified that age, Body Mass Index (BMI), smoking status, and treatment pill intake were pinpointed as critical characteristics influencing the survival of breast cancer patients. Among all models, the eXtreme Gradient Boosting (XGBoost) model demonstrated the highest predictive performance (3 years: AUC = 0.748, 6 years: AUC = 0.749, 9 years: AUC = 0.765). Kaplan-Meier analysis showed that patients classified as high-risk based on the median risk score had significantly worse survival outcomes (P < 0.0001). SHAP analysis further confirmed the dominant influence of key characteristics on mortality risk.
conclusionsThis study highlights the prognostic value of lifestyle habits in breast cancer survival. By integrating routine health indicators into interpretable machine learning models, our findings provide a practical foundation for individualized risk assessment and lifestyle-based intervention strategies.
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