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Enhancing Hypertension Risk Diagnosis Using a Hybrid Machine Learning Framework: Leveraging Body Composition Data.

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Related Experiment Video

Updated: Mar 27, 2026

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis
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Enhancing Hypertension Risk Diagnosis Using a Hybrid Machine Learning Framework: Leveraging Body Composition Data.

Abdul Wahid Mirzaye1, Hamid Saadatfar1, Mohammad Ali Nematollahi2

  • 1Department of Computer Engineering, Faculty of Electrical and Computer Engineering, University of Birjand, Birjand, Iran, birjand.ac.ir.

Biomed Research International
|March 25, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a hybrid machine learning model for hypertension risk prediction using body composition. The framework enhances early detection and personalized healthcare through improved accuracy and interpretability.

Keywords:
Gaussian naive BayesK-Means clusteringSMOTE techniquebody composition datahypertension diagnosisrandom search optimization

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Area of Science:

  • Biomedical Informatics
  • Machine Learning in Healthcare
  • Cardiovascular Disease Research

Background:

  • Hypertension is a major global health issue, leading to severe complications.
  • Current prediction methods may lack interpretability and reliability.
  • Noninvasive body composition features offer a promising avenue for risk assessment.

Purpose of the Study:

  • To develop and evaluate a dual-scenario hybrid machine learning framework for hypertension risk prediction.
  • To enhance both the interpretability and predictive reliability of hypertension risk assessment.
  • To explore the utility of noninvasive body composition features in personalized hypertension risk prediction.

Main Methods:

  • Implemented a hybrid machine learning framework with unsupervised clustering (K-Means) and supervised classification (SVM, ExtraTrees, etc.).
  • Scenario 1: Identified physiological subgroups within hypertensive individuals.
  • Scenario 2: Performed binary classification between healthy and hypertensive subjects using cluster-augmented data.

Main Results:

  • Scenario 1 identified five distinct hypertensive subgroups with significant intercluster variability (p < 0.001).
  • Scenario 2, using ExtraTrees classifier on cluster-augmented data, achieved high accuracy (98.23%) and AUC (99.87%).
  • Both clustering and feature selection improved model generalization, especially for ensemble methods.

Conclusions:

  • Integrating unsupervised clustering with supervised classification provides a robust and explainable framework for hypertension risk prediction.
  • The proposed hybrid model enhances early detection and supports precision healthcare initiatives.
  • Noninvasive body composition features, when analyzed with advanced ML, are valuable for personalized risk assessment.