ArticleBMC geriatrics2025
A prediction study on the occurrence risk of heart disease in older hypertensive patients based on machine learning.
Article in BMC geriatrics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- The burden of hypertensive heart disease in China and G20 countries: analysis from 1990 to 2021 and 30-year projections for China.Frontiers in cardiovascular medicine · 2026Article
- Cardiovascular Risk Prediction in Older Adults.Current atherosclerosis reports · 2025Review
- Frailty prediction in patients with chronic digestive system diseases: based on multi-task learning model.Frontiers in medicine · 2025Article
- A study on the risk prediction of heart disease in diabetes patients based on machine learning.Science progressArticle
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Abstract
objectiveConstructing a predictive model for the occurrence of heart disease in elderly hypertensive individuals, aiming to provide early risk identification.
methodsA total of 934 participants aged 60 and above from the China Health and Retirement Longitudinal Study with a 7-year follow-up (2011-2018) were included. Machine learning methods (logistic regression, XGBoost, DNN) were employed to build a model predicting heart disease risk in hypertensive patients. Model performance was comprehensively assessed using discrimination, calibration, and clinical decision curves.
resultsAfter a 7-year follow-up of 934 older hypertensive patients, 243 individuals (26.03%) developed heart disease. Older hypertensive patients with baseline comorbid dyslipidemia, chronic pulmonary diseases, arthritis or rheumatic diseases faced a higher risk of future heart disease. Feature selection significantly improved predictive performance compared to the original variable set. The ROC-AUC for logistic regression, XGBoost, and DNN were 0.60 (95% CI: 0.53-0.68), 0.64 (95% CI: 0.57-0.71), and 0.67 (95% CI: 0.60-0.73), respectively, with logistic regression achieving optimal calibration. XGBoost demonstrated the most noticeable clinical benefit as the threshold increased.
conclusionMachine learning effectively identifies the risk of heart disease in older hypertensive patients based on data from the CHARLS cohort. The results suggest that older hypertensive patients with comorbid dyslipidemia, chronic pulmonary diseases, and arthritis or rheumatic diseases have a higher risk of developing heart disease. This information could facilitate early risk identification for future heart disease in older hypertensive patients.
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