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Schjerven FE, Lindseth F, Steinsland I. Prognostic risk models for incident hypertension: A PRISMA systematic review and meta-analysis. PLoS One 2024; 19:e0294148. [PMID: 38466745 PMCID: PMC10927109 DOI: 10.1371/journal.pone.0294148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 10/26/2023] [Indexed: 03/13/2024] Open
Abstract
OBJECTIVE Our goal was to review the available literature on prognostic risk prediction for incident hypertension, synthesize performance, and provide suggestions for future work on the topic. METHODS A systematic search on PUBMED and Web of Science databases was conducted for studies on prognostic risk prediction models for incident hypertension in generally healthy individuals. Study-quality was assessed using the Prediction model Risk of Bias Assessment Tool (PROBAST) checklist. Three-level meta-analyses were used to obtain pooled AUC/C-statistic estimates. Heterogeneity was explored using study and cohort characteristics in meta-regressions. RESULTS From 5090 hits, we found 53 eligible studies, and included 47 in meta-analyses. Only four studies were assessed to have results with low risk of bias. Few models had been externally validated, with only the Framingham risk model validated more than thrice. The pooled AUC/C-statistics were 0.82 (0.77-0.86) for machine learning models and 0.78 (0.76-0.80) for traditional models, with high heterogeneity in both groups (I2 > 99%). Intra-class correlations within studies were 60% and 90%, respectively. Follow-up time (P = 0.0405) was significant for ML models and age (P = 0.0271) for traditional models in explaining heterogeneity. Validations of the Framingham risk model had high heterogeneity (I2 > 99%). CONCLUSION Overall, the quality of included studies was assessed as poor. AUC/C-statistic were mostly acceptable or good, and higher for ML models than traditional models. High heterogeneity implies large variability in the performance of new risk models. Further, large heterogeneity in validations of the Framingham risk model indicate variability in model performance on new populations. To enable researchers to assess hypertension risk models, we encourage adherence to existing guidelines for reporting and developing risk models, specifically reporting appropriate performance measures. Further, we recommend a stronger focus on validation of models by considering reasonable baseline models and performing external validations of existing models. Hence, developed risk models must be made available for external researchers.
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Affiliation(s)
- Filip Emil Schjerven
- Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Frank Lindseth
- Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Ingelin Steinsland
- Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim, Norway
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Schjerven FE, Ingeström EML, Steinsland I, Lindseth F. Development of risk models of incident hypertension using machine learning on the HUNT study data. Sci Rep 2024; 14:5609. [PMID: 38454041 PMCID: PMC10920790 DOI: 10.1038/s41598-024-56170-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 03/03/2024] [Indexed: 03/09/2024] Open
Abstract
In this study, we aimed to create an 11-year hypertension risk prediction model using data from the Trøndelag Health (HUNT) Study in Norway, involving 17 852 individuals (20-85 years; 38% male; 24% incidence rate) with blood pressure (BP) below the hypertension threshold at baseline (1995-1997). We assessed 18 clinical, behavioral, and socioeconomic features, employing machine learning models such as eXtreme Gradient Boosting (XGBoost), Elastic regression, K-Nearest Neighbor, Support Vector Machines (SVM) and Random Forest. For comparison, we used logistic regression and a decision rule as reference models and validated six external models, with focus on the Framingham risk model. The top-performing models consistently included XGBoost, Elastic regression and SVM. These models efficiently identified hypertension risk, even among individuals with optimal baseline BP (< 120/80 mmHg), although improvement over reference models was modest. The recalibrated Framingham risk model outperformed the reference models, approaching the best-performing ML models. Important features included age, systolic and diastolic BP, body mass index, height, and family history of hypertension. In conclusion, our study demonstrated that linear effects sufficed for a well-performing model. The best models efficiently predicted hypertension risk, even among those with optimal or normal baseline BP, using few features. The recalibrated Framingham risk model proved effective in our cohort.
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Affiliation(s)
- Filip Emil Schjerven
- Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway.
| | - Emma Maria Lovisa Ingeström
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
| | - Ingelin Steinsland
- Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Frank Lindseth
- Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway
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Liu S, Lu L, Wang F, Han B, Ou L, Gao X, Luo Y, Huo W, Zeng Q. Building a predictive model for hypertension related to environmental chemicals using machine learning. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:4595-4605. [PMID: 38105323 DOI: 10.1007/s11356-023-31384-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 12/01/2023] [Indexed: 12/19/2023]
Abstract
Hypertension is a chronic cardiovascular disease characterized by elevated blood pressure that can lead to a number of complications. There is evidence that the numerous environmental substances to which humans are exposed facilitate the emergence of diseases. In this work, we sought to investigate the relationship between exposure to environmental contaminants and hypertension as well as the predictive value of such exposures. The National Health and Nutrition Survey (NHANES) provided us with the information we needed (2005-2012). A total of 4492 participants were included in our study, and we incorporated more common environmental chemicals and covariates by feature selection followed by regularized network analysis. Then, we applied various machine learning (ML) methods, such as extreme gradient boosting (XGBoost), random forest classifier (RF), logistic regression (LR), multilayer perceptron (MLP), and support vector machine (SVM), to predict hypertension by chemical exposure. Finally, SHapley Additive exPlanations (SHAP) were further applied to interpret the features. After the initial feature screening, we included a total of 29 variables (including 21 chemicals) for ML. The areas under the curve (AUCs) of the five ML models XGBoost, RF, LR, MLP, and SVM were 0.729, 0.723, 0.721, 0.730, and 0.731, respectively. Butylparaben (BUP), propylparaben (PPB), and 9-hydroxyfluorene (P17) were the three factors in the prediction model with the highest SHAP values. Comparing five ML models, we found that environmental exposure may play an important role in hypertension. The assessment of important chemical exposure parameters lays the groundwork for more targeted therapies, and the optimized ML models are likely to predict hypertension.
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Affiliation(s)
- Shanshan Liu
- Health Management Institute, The Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, No. 28, Fuxing Road, Haidian District, Beijing, 100853, China
- Medical School of Chinese PLA, Chinese PLA General Hospital, Beijing, 100853, China
| | - Lin Lu
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Fei Wang
- Health Management Institute, The Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, No. 28, Fuxing Road, Haidian District, Beijing, 100853, China
| | - Bingqing Han
- Health Management Institute, The Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, No. 28, Fuxing Road, Haidian District, Beijing, 100853, China
- School of Medicine, Nankai University, Tianjin, 300071, China
| | - Lei Ou
- Health Management Institute, The Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, No. 28, Fuxing Road, Haidian District, Beijing, 100853, China
| | - Xiangyang Gao
- Health Management Institute, The Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, No. 28, Fuxing Road, Haidian District, Beijing, 100853, China
| | - Yi Luo
- Health Management Institute, The Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, No. 28, Fuxing Road, Haidian District, Beijing, 100853, China
| | - Wenjing Huo
- Medical Department, 305 Hospital of PLA, Beijing, 100034, China
| | - Qiang Zeng
- Health Management Institute, The Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, No. 28, Fuxing Road, Haidian District, Beijing, 100853, China.
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Islam MM, Alam MJ, Maniruzzaman M, Ahmed NAMF, Ali MS, Rahman MJ, Roy DC. Predicting the risk of hypertension using machine learning algorithms: A cross sectional study in Ethiopia. PLoS One 2023; 18:e0289613. [PMID: 37616271 PMCID: PMC10449142 DOI: 10.1371/journal.pone.0289613] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Accepted: 07/22/2023] [Indexed: 08/26/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Hypertension (HTN), a major global health concern, is a leading cause of cardiovascular disease, premature death and disability, worldwide. It is important to develop an automated system to diagnose HTN at an early stage. Therefore, this study devised a machine learning (ML) system for predicting patients with the risk of developing HTN in Ethiopia. MATERIALS AND METHODS The HTN data was taken from Ethiopia, which included 612 respondents with 27 factors. We employed Boruta-based feature selection method to identify the important risk factors of HTN. The four well-known models [logistics regression, artificial neural network, random forest, and extreme gradient boosting (XGB)] were developed to predict HTN patients on the training set using the selected risk factors. The performances of the models were evaluated by accuracy, precision, recall, F1-score, and area under the curve (AUC) on the testing set. Additionally, the SHapley Additive exPlanations (SHAP) method is one of the explainable artificial intelligences (XAI) methods, was used to investigate the associated predictive risk factors of HTN. RESULTS The overall prevalence of HTN patients is 21.2%. This study showed that XGB-based model was the most appropriate model for predicting patients with the risk of HTN and achieved the accuracy of 88.81%, precision of 89.62%, recall of 97.04%, F1-score of 93.18%, and AUC of 0. 894. The XBG with SHAP analysis reveal that age, weight, fat, income, body mass index, diabetes mulitas, salt, history of HTN, drinking, and smoking were the associated risk factors of developing HTN. CONCLUSIONS The proposed framework provides an effective tool for accurately predicting individuals in Ethiopia who are at risk for developing HTN at an early stage and may help with early prevention and individualized treatment.
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Affiliation(s)
- Md. Merajul Islam
- Department of Statistics, Jatiya Kabi Kazi Nazrul Islam University, Trishal, Mymensingh, Bangladesh
- Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh
| | - Md. Jahangir Alam
- Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh
- Mainanalytics GmbH, Sulzbach/Taunus, Germany
| | - Md Maniruzzaman
- Statistics Discipline, Khulna University, Khulna, Bangladesh
| | | | - Md Sujan Ali
- Department of Computer Science and Engineering, Jatiya Kabi Kazi Nazrul Islam University, Trishal, Mymensingh, Bangladesh
| | | | - Dulal Chandra Roy
- Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh
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Chowdhury MZI, Leung AA, Walker RL, Sikdar KC, O’Beirne M, Quan H, Turin TC. A comparison of machine learning algorithms and traditional regression-based statistical modeling for predicting hypertension incidence in a Canadian population. Sci Rep 2023; 13:13. [PMID: 36593280 PMCID: PMC9807553 DOI: 10.1038/s41598-022-27264-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Accepted: 12/29/2022] [Indexed: 01/03/2023] Open
Abstract
Risk prediction models are frequently used to identify individuals at risk of developing hypertension. This study evaluates different machine learning algorithms and compares their predictive performance with the conventional Cox proportional hazards (PH) model to predict hypertension incidence using survival data. This study analyzed 18,322 participants on 24 candidate features from the large Alberta's Tomorrow Project (ATP) to develop different prediction models. To select the top features, we applied five feature selection methods, including two filter-based: a univariate Cox p-value and C-index; two embedded-based: random survival forest and least absolute shrinkage and selection operator (Lasso); and one constraint-based: the statistically equivalent signature (SES). Five machine learning algorithms were developed to predict hypertension incidence: penalized regression Ridge, Lasso, Elastic Net (EN), random survival forest (RSF), and gradient boosting (GB), along with the conventional Cox PH model. The predictive performance of the models was assessed using C-index. The performance of machine learning algorithms was observed, similar to the conventional Cox PH model. Average C-indexes were 0.78, 0.78, 0.78, 0.76, 0.76, and 0.77 for Ridge, Lasso, EN, RSF, GB and Cox PH, respectively. Important features associated with each model were also presented. Our study findings demonstrate little predictive performance difference between machine learning algorithms and the conventional Cox PH regression model in predicting hypertension incidence. In a moderate dataset with a reasonable number of features, conventional regression-based models perform similar to machine learning algorithms with good predictive accuracy.
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Affiliation(s)
- Mohammad Ziaul Islam Chowdhury
- grid.22072.350000 0004 1936 7697Department of Community Health Sciences, University of Calgary, 3280 Hospital Drive NW, Calgary, AB T2N 4Z6 Canada ,grid.22072.350000 0004 1936 7697Department of Family Medicine, University of Calgary, 3330 Hospital Drive NW, Calgary, AB T2N 4N1 Canada ,grid.22072.350000 0004 1936 7697Present Address: Department of Psychiatry, University of Calgary, 3280 Hospital Drive NW, Calgary, AB T2N 4Z6 Canada
| | - Alexander A. Leung
- grid.22072.350000 0004 1936 7697Department of Community Health Sciences, University of Calgary, 3280 Hospital Drive NW, Calgary, AB T2N 4Z6 Canada ,grid.22072.350000 0004 1936 7697Department of Medicine, University of Calgary, 3280 Hospital Drive NW, Calgary, AB T2N 4Z6 Canada
| | - Robin L. Walker
- grid.22072.350000 0004 1936 7697Department of Community Health Sciences, University of Calgary, 3280 Hospital Drive NW, Calgary, AB T2N 4Z6 Canada ,grid.413574.00000 0001 0693 8815Primary Health Care Integration Network, Primary Health Care, Alberta Health Services, Calgary, AB Canada
| | - Khokan C. Sikdar
- grid.413574.00000 0001 0693 8815Health Status Assessment, Surveillance and Reporting, Public Health Surveillance and Infrastructure, Provincial Population and Public Health, Alberta Health Services, 10101 Southport Rd. SW, Calgary, AB T2W 3N2 Canada
| | - Maeve O’Beirne
- grid.22072.350000 0004 1936 7697Department of Family Medicine, University of Calgary, 3330 Hospital Drive NW, Calgary, AB T2N 4N1 Canada
| | - Hude Quan
- grid.22072.350000 0004 1936 7697Department of Community Health Sciences, University of Calgary, 3280 Hospital Drive NW, Calgary, AB T2N 4Z6 Canada
| | - Tanvir C. Turin
- grid.22072.350000 0004 1936 7697Department of Community Health Sciences, University of Calgary, 3280 Hospital Drive NW, Calgary, AB T2N 4Z6 Canada ,grid.22072.350000 0004 1936 7697Department of Family Medicine, University of Calgary, 3330 Hospital Drive NW, Calgary, AB T2N 4N1 Canada
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