Key result
A Random Forest machine learning model accurately predicted elevated cardiovascular disease risk among rural postmenopausal women, achieving an accuracy of 98.91% (95% CI 97.8-99.6%).
Why the study?
Cardiovascular disease is a leading cause of morbidity and mortality among postmenopausal women in rural India, where healthcare resources remain limited.
Do machine learning models accurately predict elevated cardiovascular disease risk in rural postmenopausal women?
Population
458 rural postmenopausal women in India
Comparison
Seven machine learning models to predict elevated cardiovascular disease risk
Design
Observational cross-sectional study
Authors
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High model performance should not yet change screening practice; leaves open external validation in independent rural postmenopausal cohorts.
Cross-Sectional (n=458)
Yes
Do machine learning models accurately predict elevated cardiovascular disease risk in rural postmenopausal women?
Effect estimate: Accuracy 98.91% (95% CI 97.8-99.6)
Machine learning models, particularly Random Forest and XGBoost, demonstrated high accuracy in predicting cardiovascular disease risk among rural postmenopausal women using basic clinical and biochemical parameters.
Ghosh et al. (2026) conducted a cross-sectional in Cardiovascular disease risk (n=458). Machine learning models for CVD risk prediction was evaluated on Elevated cardiovascular disease risk (Accuracy 98.91%, 95% CI 97.8-99.6). A Random Forest machine learning model accurately predicted elevated cardiovascular disease risk among rural postmenopausal women, achieving an accuracy of 98.91% (95% CI 97.8-99.6%).
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