A prediction study on the occurrence risk of heart disease in older hypertensive patients based on machine learning
Abstract Objective Constructing a predictive model for the occurrence of heart disease in elderly hypertensive individuals, aiming to provide early risk identification. Methods A total of 934 participants aged 60 and above from the China Health and Retirement Longitudinal Study with a 7-year follow-...
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BMC
2025-01-01
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| Series: | BMC Geriatrics |
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| Online Access: | https://doi.org/10.1186/s12877-025-05679-1 |
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| author | Fei Si Qian Liu Jing Yu |
| author_facet | Fei Si Qian Liu Jing Yu |
| author_sort | Fei Si |
| collection | DOAJ |
| description | Abstract Objective Constructing a predictive model for the occurrence of heart disease in elderly hypertensive individuals, aiming to provide early risk identification. Methods A 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. Results After 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. Conclusion Machine 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. |
| format | Article |
| id | doaj-art-7e85c438029d49c1b0432e3d5b8ff886 |
| institution | Kabale University |
| issn | 1471-2318 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
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| series | BMC Geriatrics |
| spelling | doaj-art-7e85c438029d49c1b0432e3d5b8ff8862025-01-12T12:38:39ZengBMCBMC Geriatrics1471-23182025-01-0125111210.1186/s12877-025-05679-1A prediction study on the occurrence risk of heart disease in older hypertensive patients based on machine learningFei Si0Qian Liu1Jing Yu2Department of Cardiology, The Second Hospital & Clinical Medical School, Lanzhou UniversityDepartment of Cardiology, The Second Hospital & Clinical Medical School, Lanzhou UniversityDepartment of Cardiology, The Second Hospital & Clinical Medical School, Lanzhou UniversityAbstract Objective Constructing a predictive model for the occurrence of heart disease in elderly hypertensive individuals, aiming to provide early risk identification. Methods A 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. Results After 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. Conclusion Machine 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.https://doi.org/10.1186/s12877-025-05679-1HypertensionHeart DiseaseMachine LearningRisk PredictionOlder Patients |
| spellingShingle | Fei Si Qian Liu Jing Yu A prediction study on the occurrence risk of heart disease in older hypertensive patients based on machine learning BMC Geriatrics Hypertension Heart Disease Machine Learning Risk Prediction Older Patients |
| title | A prediction study on the occurrence risk of heart disease in older hypertensive patients based on machine learning |
| title_full | A prediction study on the occurrence risk of heart disease in older hypertensive patients based on machine learning |
| title_fullStr | A prediction study on the occurrence risk of heart disease in older hypertensive patients based on machine learning |
| title_full_unstemmed | A prediction study on the occurrence risk of heart disease in older hypertensive patients based on machine learning |
| title_short | A prediction study on the occurrence risk of heart disease in older hypertensive patients based on machine learning |
| title_sort | prediction study on the occurrence risk of heart disease in older hypertensive patients based on machine learning |
| topic | Hypertension Heart Disease Machine Learning Risk Prediction Older Patients |
| url | https://doi.org/10.1186/s12877-025-05679-1 |
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