Risk factors and prediction models for cardiovascular complications of hypertension in older adults with machine learning : A cross-sectional study
© 2024 The Authors. Published by Elsevier Ltd..
Background: Hypertension has emerged as a chronic disease prevalent worldwide that may cause severe cardiovascular complications, particularly in older patients. However, there is a paucity of studies that use risk factors and prediction models for cardiovascular complications associated with hypertension in older adults.
Objectives: To identify the risk factors and develop prediction models for cardiovascular complications among older patients with hypertension.
Methods: A convenience sample of 476 older patients with hypertension was recruited from a university-affiliated hospital in China. Demographic data, clinical physiological indicators, regulatory emotional self-efficacy, medication adherence, and lifestyle information were collected from participants. Binary logistic regression analysis was performed to screen for preliminary risk factors associated with cardiovascular complications. Two machine learning methods, Back-Propagation neural network, and random forest were applied to develop prediction models for cardiovascular complications among the study cohort. The sensitivity, specificity, accuracy, receiver operating characteristic curve, and area under the curve (AUC) values were used to assess the performance of the prediction models.
Results: Binary logistic regression identified nine risk factors for cardiovascular complications among older patients with hypertension. The machine learning models displayed excellent performance in predicting cardiovascular complications, with the random forest model (AUC 0.954) outperforming the Back-Propagation neural network model (AUC 0.811), as confirmed by model comparison analysis. The sensitivity, specificity and accuracy of the Back-Propagation neural network model compared to the random forest model were 74.2% vs. 86.5%, 75.2% vs. 94.3%, and 74.7% vs. 90.4%, respectively.
Conclusion: The machine learning methods employed in this study demonstrated feasibility in predicting cardiovascular complications among older patients with hypertension, with the random forest model based on nine risk factors exhibiting excellent prediction performance. These models could be used to identify high-risk populations and suggest early interventions aimed at preventing cardiovascular complications in such cohorts.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:10 |
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| Enthalten in: |
Heliyon - 10(2024), 6 vom: 30. März, Seite e27941 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wu, Yixin [VerfasserIn] |
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| Links: |
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| Themen: |
Back-propagation neural network |
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| Anmerkungen: |
Date Revised 22.03.2024 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1016/j.heliyon.2024.e27941 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a © 2024 The Authors. Published by Elsevier Ltd. | ||
| 520 | |a Background: Hypertension has emerged as a chronic disease prevalent worldwide that may cause severe cardiovascular complications, particularly in older patients. However, there is a paucity of studies that use risk factors and prediction models for cardiovascular complications associated with hypertension in older adults | ||
| 520 | |a Objectives: To identify the risk factors and develop prediction models for cardiovascular complications among older patients with hypertension | ||
| 520 | |a Methods: A convenience sample of 476 older patients with hypertension was recruited from a university-affiliated hospital in China. Demographic data, clinical physiological indicators, regulatory emotional self-efficacy, medication adherence, and lifestyle information were collected from participants. Binary logistic regression analysis was performed to screen for preliminary risk factors associated with cardiovascular complications. Two machine learning methods, Back-Propagation neural network, and random forest were applied to develop prediction models for cardiovascular complications among the study cohort. The sensitivity, specificity, accuracy, receiver operating characteristic curve, and area under the curve (AUC) values were used to assess the performance of the prediction models | ||
| 520 | |a Results: Binary logistic regression identified nine risk factors for cardiovascular complications among older patients with hypertension. The machine learning models displayed excellent performance in predicting cardiovascular complications, with the random forest model (AUC 0.954) outperforming the Back-Propagation neural network model (AUC 0.811), as confirmed by model comparison analysis. The sensitivity, specificity and accuracy of the Back-Propagation neural network model compared to the random forest model were 74.2% vs. 86.5%, 75.2% vs. 94.3%, and 74.7% vs. 90.4%, respectively | ||
| 520 | |a Conclusion: The machine learning methods employed in this study demonstrated feasibility in predicting cardiovascular complications among older patients with hypertension, with the random forest model based on nine risk factors exhibiting excellent prediction performance. These models could be used to identify high-risk populations and suggest early interventions aimed at preventing cardiovascular complications in such cohorts | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Back-propagation neural network | |
| 650 | 4 | |a Cardiovascular complications | |
| 650 | 4 | |a Hypertension | |
| 650 | 4 | |a Older adults | |
| 650 | 4 | |a Random forest | |
| 650 | 4 | |a Risk factors | |
| 700 | 1 | |a Xin, Bo |e verfasserin |4 aut | |
| 700 | 1 | |a Wan, Qiuyuan |e verfasserin |4 aut | |
| 700 | 1 | |a Ren, Yanping |e verfasserin |4 aut | |
| 700 | 1 | |a Jiang, Wenhui |e verfasserin |4 aut | |
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