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A Comparison of Machine Learning Algorithms for Predicting Hypertension Incidence Based on Cohort Study
Somayeh Ghiasi1, Susan Darroudi2, Mina Moradi3
1Department of Biostatistics, Faculty of Health, Mashhad University of Medical Sciences, Mashhad, Iran.
Endocrinology, Diabetes & Metabolism
|May 10, 2026
Summary
Machine learning, specifically XGBoost, accurately predicted hypertension (HTN) development. Key risk factors identified include age, copper, BMI, triglycerides, HDL, glucose, and uric acid for better HTN prevention.
Area of Science:
- Cardiovascular disease research
- Biostatistics and Machine Learning applications
Background:
- Hypertension (HTN) remains a significant global health concern, necessitating improved prediction and prevention strategies.
- Traditional risk factor assessment may not fully capture the complexity of HTN development.
Purpose of the Study:
- To identify key hypertension risk factors using machine learning (ML) models.
- To enhance the accuracy of hypertension prediction through advanced computational methods.
Main Methods:
- Analysis of the Mashhad Stroke and Heart Atherosclerotic Disorder (MASHAD) cohort data over 10 years.
- Application of five ML algorithms: K-nearest neighbors (KNN), logistic regression (LR), XGBoost (XGB), random forest (RF), and neural networks (NN).
- Identification of influential risk factors using SHAP feature importance analysis.
Main Results:
- The XGBoost model demonstrated superior performance in predicting HTN incidence, achieving an AUC-ROC of 0.79 and 74% accuracy.
- Key predictors consistently identified by ML models were age, copper, body mass index (BMI), triglycerides, HDL, glucose, and uric acid.
- The XGBoost model effectively predicted HTN development over a 10-year follow-up period.
Conclusions:
- Machine learning models, particularly XGBoost, offer a powerful tool for predicting hypertension.
- Age, copper levels, BMI, lipid profiles (triglycerides, HDL), glucose, and uric acid are significant modifiable and non-modifiable risk factors for hypertension.
- Integrating ML into hypertension prediction and prevention strategies is crucial for public health.
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