Lifestyle habits including age, BMI, smoking status, and treatment pill intake predict breast cancer survival in female patients
This study analyzed 21,219 female breast cancer patients from the UK Biobank and found that age, BMI, smoking status, and treatment pill intake are key lifestyle-related factors influencing survival. The XGBoost model achieved AUCs of 0.748, 0.749, and 0.765 at 3, 6, and 9 years, respectively, and high-risk patients had significantly worse survival (P < 0.0001).
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Lifestyle habits including age, BMI, smoking status, and treatment pill intake predict breast cancer survival in female patients The current body of evidence comprises 1 study. EvidenceHub rates the overall confidence at 32/100 (low).
The Claim
Lifestyle habits including age, BMI, smoking status, and treatment pill intake predict breast cancer survival in female patients
This conclusion is most relevant to: 21,219 female breast cancer patients from the UK Biobank.
What the Research Shows
The conclusion draws on 1 linked study. Highlights from the cited literature:
- ▸Interpretable machine-learning survival prediction of breast cancer prognosis from lifestyle factors: evidence from UK biobank. (Breast cancer (Tokyo, Japan), 2026) —
How It Works
The proposed biological pathway:
- ▸Lifestyle factors (age, BMI, smoking, treatment pill intake) are identified as key prognostic variables
- ▸Machine learning model (XGBoost) integrates these factors to calculate risk scores
- ▸High-risk patients based on median risk score show significantly worse survival outcomes
- ▸SHAP analysis confirms dominant influence of these characteristics on mortality risk
Who Might Benefit
Evidence fit by population:
- ▸21,219 female breast cancer patients from the UK Biobank
Recommended Dose
N/A
Limitations & Caveats
Important context when interpreting this evidence:
- ▸Observational study design cannot establish causality between lifestyle habits and survival
- ▸Sleep quality was mentioned but not identified as a key feature in the final model, limiting specific sleep-related conclusions
Frequently Asked Questions
What lifestyle factors were most important for predicting breast cancer survival?▼
Age, BMI, smoking status, and treatment pill intake were identified as critical characteristics influencing survival.
How well did the machine learning model predict survival?▼
The XGBoost model achieved AUCs of 0.748 at 3 years, 0.749 at 6 years, and 0.765 at 9 years, indicating moderate predictive performance.
Was sleep quality a significant predictor in this study?▼
Sleep quality was included in the analysis but was not among the top features selected; age, BMI, smoking, and treatment pill intake were the key predictors.
What population was studied?▼
The study analyzed 21,219 female breast cancer patients from the UK Biobank, a large-scale population-based cohort.
References
- 1.Yao Y, Zhai X, Liang Z, Lam CK, Xie H, Tong HHY, Tong T, Chen Y, Mann RM, He M, Li K, Tan T. “Interpretable machine-learning survival prediction of breast cancer prognosis from lifestyle factors: evidence from UK biobank..” Breast cancer (Tokyo, Japan), 2026. PMID: 42467394 DOI: 10.1007/s12282-026-01893-w