ArticleScientific reports2023
A comparison of machine learning algorithms and traditional regression-based statistical modeling for predicting hypertension incidence in a Canadian population.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 2 of them syntheses that pooled it.
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Who cites it
28 citing papers in PubMed, 2 syntheses or guidelines pooled it, 52 citations in OpenAlex.
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- Risk factor-targeted abdominal aortic aneurysm screening: systematic review of risk prediction for abdominal aortic aneurysm.The British journal of surgery · 2024Pooled it
- Comparison of the Cox Proportional Hazards Model and Random Survival Forest Algorithm for Predicting Patient-Specific Survival Probabilities in Clinical Trial Data.Biometrical journal. Biometrische Zeitschrift · 2026Article
- Machine learning water quality inversion via optimal band selection from highly correlated subsets.iScience · 2026Article
- Multi-horizon machine learning for population-level hypertension risk stratification in the UK Biobank.European heart journal. Digital health · 2026Article
- Leakage-Controlled and Survey-Weighted Machine Learning for Neonatal Mortality Risk Prediction Using NFHS-5 Data.Healthcare (Basel, Switzerland) · 2026Article
- Machine learning approaches to identify influential factors associated with hypertension and prehypertension among rural adults in Bangladesh.Journal of public health research · 2026Article
- Machine learning model for predicting improvement in left ventricular systolic function in patients with heart failure and reduced ejection fraction.International journal of cardiology. Heart & vasculature · 2026Article
- Incorporating geospatial environmental exposure indicators in individual hypertension risk prediction: a multi-stage machine learning pipeline.Journal of exposure science & environmental epidemiology · 2026Article
- A hybrid LSTM-GRU framework for lung cancer classification using GWO-WOA algorithm for hyperparameter tuning and BPSO for feature selection.Scientific reports · 2026Article
- Predicting health-related quality of life two years post-diagnosis across seven cancer types: using machine learning to identify vulnerable patients.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2026Article
- Exploring the correlates of COVID-19 vaccination inequity: a global analysis using machine learning from a health economic lens.Frontiers in health services · 2026Article
- Machine Learning-Enabled Rapid Assessment of Plant-Based Protein Digestibility Through Physicochemical Profiles.Foods (Basel, Switzerland) · 2025Article
- Machine learning to predict the role of CHWs in shifting birth preferences away from homebirth in India.Scientific reports · 2025Article
- Development and Validation of Prognostic Models for Treatment Response of Patients with B-Cell Lymphoma: Standard Statistical and Machine-Learning Approaches.Journal of clinical medicine · 2025Article
- Article
- Predicting hypertension and identifying most important factors among married women in Bangladesh using machine learning approach.PloS one · 2025Article
- Machine learning-based prediction of 6-month functional recovery in hypertensive cerebral hemorrhage: insights from XGBoost and SHAP analysis.Frontiers in neurology · 2025Article
- Enhancing Clinical Decision Making by Predicting Readmission Risk in Patients With Heart Failure Using Machine Learning: Predictive Model Development Study.JMIR medical informatics · 2024Article
- Identifying Significant SNPs of the Total Number of Piglets Born and Their Relationship with Leg Bumps in Pigs.Biology · 2024Article
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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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