ArticleScientific reports2023
Body composition predicts hypertension using machine learning methods: a cohort study.
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 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.
- Predictive variables and diagnostic performance of cross-sectional models for hypertension detection: a systematic review.Frontiers in cardiovascular medicine · 2025Pooled it
- Duplicate-Aware Internal Validation of Machine-Learning Models for Classifying a Tanita BIA-Derived High-Adiposity Phenotype Using Simple Anthropometric Predictors.Journal of clinical medicine · 2026Article
- BodyMAE: A Surface-Area Aware Masked Autoencoder for Body Composition Estimation from 3D Body Scans.medRxiv : the preprint server for health sciences · 2026Article
- A Comparison of Machine Learning Algorithms for Predicting Hypertension Incidence Based on Cohort Study.Endocrinology, diabetes & metabolism · 2026Article
- Integrating machine learning for advanced analysis of bioelectrical impedance parameters in children with nephrotic syndrome.Frontiers in pediatrics · 2026Article
- Mediating Role of Insulin Resistance in the Association Between Body Composition and Hypertension.High blood pressure & cardiovascular prevention : the official journal of the Italian Society of Hypertension · 2026Article
- Enhancing Hypertension Risk Diagnosis Using a Hybrid Machine Learning Framework: Leveraging Body Composition Data.BioMed research international · 2026Article
- Relationship Between Body Composition and Hypertension in Postmenopausal Women with Osteoporosis-A Single-Center Cross-Sectional Study.Diabetes, metabolic syndrome and obesity : targets and therapy · 2025Article
- Machine Learning Approach for Predicting Hypertension Based on Body Composition in South Korean Adults.Bioengineering (Basel, Switzerland) · 2024Article
- Transforming Healthcare: The AI Revolution in the Comprehensive Care of Hypertension.Clinics and practice · 2024Review
- Opportunistic CT for Prediction of Adverse Postoperative Events in Patients with Spinal Metastases.Diagnostics (Basel, Switzerland) · 2024Article
- Transforming the cardiometabolic disease landscape: Multimodal AI-powered approaches in prevention and management.Cell metabolism · 2024Review
- Machine learning-based models to predict the conversion of normal blood pressure to hypertension within 5-year follow-up.PloS one · 2024Article
- Prediction of in-hospital mortality risk for patients with acute ST-elevation myocardial infarction after primary PCI based on predictors selected by GRACE score and two feature selection methods.Frontiers in cardiovascular medicine · 2024Article
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Authors and funding
13 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We used machine learning methods to investigate if body composition indices predict hypertension. Data from a cohort study was used, and 4663 records were included (2156 were male, 1099 with hypertension, with the age range of 35-70 years old). Body composition analysis was done using bioelectrical impedance analysis (BIA); weight, basal metabolic rate, total and regional fat percentage (FATP), and total and regional fat-free mass (FFM) were measured. We used machine learning methods such as Support Vector Classifier, Decision Tree, Stochastic Gradient Descend Classifier, Logistic Regression, Gaussian Naïve Bayes, K-Nearest Neighbor, Multi-Layer Perceptron, Random Forest, Gradient Boosting, Histogram-based Gradient Boosting, Bagging, Extra Tree, Ada Boost, Voting, and Stacking to classify the investigated cases and find the most relevant features to hypertension. FATP, AFFM, BMR, FFM, TRFFM, AFATP, LFATP, and older age were the top features in hypertension prediction. Arm FFM, basal metabolic rate, total FFM, Trunk FFM, leg FFM, and male gender were inversely associated with hypertension, but total FATP, arm FATP, leg FATP, older age, trunk FATP, and female gender were directly associated with hypertension. AutoMLP, stacking and voting methods had the best performance for hypertension prediction achieving an accuracy rate of 90%, 84% and 83%, respectively. By using machine learning methods, we found that BIA-derived body composition indices predict hypertension with acceptable accuracy.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.