Enhancing Hypertension Risk Diagnosis Using a Hybrid Machine Learning Framework: Leveraging Body Composition Data.
Hypertension, widely recognized as the "silent killer," remains a leading cause of cardiovascular, renal, and neurological complications worldwide. This study proposes a dual‐scenario hybrid machine learning framework for hypertension risk prediction using noninvasive body composition features, aime...
| Publicado en: | BioMed Research International Vol. 2026; pp. 1 - 25 |
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| Autores principales: | , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
2/1/2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=191298417&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 191298417 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 2/1/2026 vid: 2026 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 191298417 191298417 191298417 10.1155/bmri/6335947 191298417 ppf: 1 ppct: 24 formats: fmt: – @attributes: type: T – @attributes: type: C – @attributes: type: P tig: atl: Enhancing Hypertension Risk Diagnosis Using a Hybrid Machine Learning Framework: Leveraging Body Composition Data. aug: au: Mirzaye, Abdul Wahid Saadatfar, Hamid Nematollahi, Mohammad Ali Banerjee, Baisakhi affil: Department of Computer Engineering,, Faculty of Electrical and Computer Engineering,, University of Birjand,, Birjand, Iran, birjand.ac.ir sug: subj: Hypertension Diagnosis Hypertension Risk Factors Machine Learning Body Composition Risk Assessment Predictive Value of Tests Iran Human Male Female Adult Middle Age Aged Descriptive Statistics ROC Curve Paired T-Tests Post Hoc Analysis Decision Trees Random Forest Boosting Machine Learning Algorithms Logistic Regression Methodological Research Analysis of Variance Age Factors Sex Factors Cluster Analysis Reproducibility of Results Sensitivity and Specificity Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Male Female ab: Hypertension, widely recognized as the "silent killer," remains a leading cause of cardiovascular, renal, and neurological complications worldwide. This study proposes a dual‐scenario hybrid machine learning framework for hypertension risk prediction using noninvasive body composition features, aimed at enhancing both interpretability and predictive reliability. In Scenario 1, an unsupervised clustering analysis inspired by self‐labeling principles was performed exclusively on hypertensive individuals, where five physiological subgroups were identified via K‐Means clustering and validated using Silhouette (0.3371), Davies–Bouldin (1.0094), and Calinski–Harabasz (720.10) indices. Significant intercluster variability (p < 0.001) was observed across key indicators such as FATP, RLFATP, LLFATP, FATM, and age. Among the tested models, the support vector machine (SVM) with random oversampling achieved the best performance (accuracy = 99.08%, F1 = 98.04%, AUC = 99.98%), confirming effective subgroup discrimination. In Scenario 2, a comprehensive binary classification between healthy and hypertensive subjects was conducted using five models—ExtraTrees, KNN, SVM, Gaussian Naive Bayes, and Decision Tree—across multiple configurations. The cluster‐augmented dataset yielded the best results, with the ExtraTrees classifier achieving superior performance (accuracy = 98.23%, recall = 98.30%, precision = 98.17%, F1 = 98.23%, AUC = 99.87%). Clustering and feature selection both improved generalization, particularly for ensemble‐based learners. Overall, Scenario 2 demonstrated the highest predictive accuracy and stability, whereas Scenario 1 provided valuable interpretability through subgroup discovery. These findings highlight that integrating unsupervised clustering with supervised classification offers a robust and explainable framework for personalized hypertension risk prediction, contributing to early detection and precision healthcare. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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