| Title: | A lightweight machine learning approach for predicting breast cancer risk based on routine clinical indicators in the Chinese population. |
| Journal: | Health Informatics Journal |
| Published: | 1 Jul 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/42647514/ |
| DOI: | https://doi.org/10.1177/14604582261477932 |
| URL: | http://dx.doi.org/10.1177/14604582261477932 |
Publication 21450
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Abstract
BackgroundConventional breast cancer screening is limited by cost, access, and patient discomfort. Whether a lightweight machine-learning model trained on routine laboratory indicators can support opportunistic screening remains unsettled.MethodsA retrospective cohort of 13,285 individuals (1,216 ICD-10 C50 cases, 12,069 controls) from a Chinese tertiary hospital during 2022 to 2024 was analysed with a leakage-controlled pipeline. Six classical learners and LightGBM were trained on twenty indicators, then assessed by discrimination, calibration, decision-curve analysis, and zero-shot UK Biobank validation.ResultsThe lightweight LightGBM achieved an internal AUC of 0.987 with sensitivity 0.835, specificity 0.983, Brier 0.026, and an external AUC of 0.83, training in 5.6 s within 1.65 MB.ConclusionThe model is a candidate low-cost triage tool, pending multicentre prospective validation and recalibration.</p>
13 Keywords
- Boosting Machine Learning Algorithms
- Breast Neoplasms
- China
- Classification Algorithms
- East Asian People
- Female
- Humans
- Machine Learning
- Middle Aged
- Prediction Algorithms
- Predictive Learning Models
- Retrospective Studies
- Risk Assessment
2 Authors
- Duo Dai
- Min Ma
Enabling scientific discoveries that improve human health