ArticleJMIR medical informatics2020
Predicting Breast Cancer in Chinese Women Using Machine Learning Techniques: Algorithm Development.
Article in JMIR medical informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of prediction models for risk of breast cancer.BMC cancer · 2025Pooled it
- Development and External Validation of an Interpretable Machine Learning Model Using Routine Blood Biomarkers for Differentiating Malignant Breast Nodules.Journal of inflammation research · 2026Article
- Integrating Serum Lipid Biomarkers Into Machine Learning for the Differential Diagnosis of Breast Nodules.The breast journal · 2026Article
- Predicting Childhood Anaemia in Nigeria: A Machine Learning Approach to Uncover Key Risk Factors.Public health challenges · 2025Article
- Framework for Race-Specific Prostate Cancer Detection Using Machine Learning Through Gene Expression Data: Feature Selection Optimization Approach.JMIR bioinformatics and biotechnology · 2025Article
- Article
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- Topological embedding and directional feature importance in ensemble classifiers for multi-class classification.Computational and structural biotechnology journal · 2024Article
- Mortality Prediction Modeling for Patients with Breast Cancer Based on Explainable Machine Learning.Cancers · 2024Article
- The Value of Smartwatches in the Health Care Sector for Monitoring, Nudging, and Predicting: Viewpoint on 25 Years of Research.Journal of medical Internet research · 2024Review
- Mean Heart Dose Prediction Using Parameters of Single-Slice Computed Tomography and Body Mass Index: Machine Learning Approach for Radiotherapy of Left-Sided Breast Cancer of Asian Patients.Current oncology (Toronto, Ont.) · 2023Article
- Multi-algorithms analysis for pre-treatment prediction of response to transarterial chemoembolization in hepatocellular carcinoma on multiphase MRI.Insights into imaging · 2023Article
- Multi-classification of national fitness test grades based on statistical analysis and machine learning.PloS one · 2023Article
- Over-the-Counter Breast Cancer Classification Using Machine Learning and Patient Registration Records.Diagnostics (Basel, Switzerland) · 2022Article
- The Impact of Artificial Intelligence on Health Equity in Oncology: Scoping Review.Journal of medical Internet research · 2022Article
- Predicting Readmission Charges Billed by Hospitals: Machine Learning Approach.JMIR medical informatics · 2022Article
- Prediction of Breast Cancer using Machine Learning Approaches.Journal of biomedical physics & engineering · 2022Article
- Comparison of Machine Learning Algorithms in the Prediction of Hospitalized Patients with Schizophrenia.Sensors (Basel, Switzerland) · 2022Article
- Breast Cancer Surgery 10-Year Survival Prediction by Machine Learning: A Large Prospective Cohort Study.Biology · 2021Article
- Study the Effect of the Risk Factors in the Estimation of the Breast Cancer Risk Score Using Machine Learning.Asian Pacific journal of cancer prevention : APJCP · 2021Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRisk-based breast cancer screening is a cost-effective intervention for controlling breast cancer in China, but the successful implementation of such intervention requires an accurate breast cancer prediction model for Chinese women.
objectiveThis study aimed to evaluate and compare the performance of four machine learning algorithms on predicting breast cancer among Chinese women using 10 breast cancer risk factors.
methodsA dataset consisting of 7127 breast cancer cases and 7127 matched healthy controls was used for model training and testing. We used repeated 5-fold cross-validation and calculated AUC, sensitivity, specificity, and accuracy as the measures of the model performance.
resultsThe three novel machine-learning algorithms (XGBoost, Random Forest and Deep Neural Network) all achieved significantly higher area under the receiver operating characteristic curves (AUCs), sensitivity, and accuracy than logistic regression. Among the three novel machine learning algorithms, XGBoost (AUC 0.742) outperformed deep neural network (AUC 0.728) and random forest (AUC 0.728). Main residence, number of live births, menopause status, age, and age at first birth were considered as top-ranked variables in the three novel machine learning algorithms.
conclusionsThe novel machine learning algorithms, especially XGBoost, can be used to develop breast cancer prediction models to help identify women at high risk for breast cancer in developing countries.
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