What question did this study set out to answer?

This study aims to develop a machine learning model for classifying breast cancer using various patient data.

May 20, 2026International Journal of Breast Cancer1 citationsOpen Access

Machine Learning Prediction of Breast Cancer Using Clinical and Lifestyle Data Insights

Machine Learning–Based Prediction of Breast Cancer in Women: Insights From Feature Selection of Clinical and Lifestyle Data

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Randomized trial evaluates machine learning for breast cancer classification, suggesting effective risk assessment potential.

This study aims to develop a machine learning model for classifying breast cancer using various patient data.
Retrospective analysis using demographic, clinical, and lifestyle data from a case-control study.
Feature selection applied to reduce 414 variables to 40 key predictors via mutual information and ANOVA.
Supervised learning techniques including Random Forest and Support Vector Machine were employed.
Random Forest model achieved the highest accuracy of 0.9897, indicating excellent classification performance.
Significant demographic and clinical differences were found between breast cancer patients and healthy controls (p < 0.001).
Feature selection identified key predictors such as genetic factors, reproductive characteristics, and lifestyle behaviors.

Allahqoli et al. (2026) studied this question.

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May 20, 2026International Journal of Breast Cancer1 citationsOpen Access

Machine Learning–Based Prediction of Breast Cancer in Women: Insights From Feature Selection of Clinical and Lifestyle Data

View Full Paper

Ask AI

Bookmark

Authors

Randomized trial evaluates machine learning for breast cancer classification, suggesting effective risk assessment potential.

This study aims to develop a machine learning model for classifying breast cancer using various patient data.
Retrospective analysis using demographic, clinical, and lifestyle data from a case-control study.
Feature selection applied to reduce 414 variables to 40 key predictors via mutual information and ANOVA.
Supervised learning techniques including Random Forest and Support Vector Machine were employed.
Random Forest model achieved the highest accuracy of 0.9897, indicating excellent classification performance.
Significant demographic and clinical differences were found between breast cancer patients and healthy controls (p < 0.001).
Feature selection identified key predictors such as genetic factors, reproductive characteristics, and lifestyle behaviors.

Allahqoli et al. (2026) studied this question.

View Full Paper

Ask AI

Bookmark