Body composition predicts hypertension using machine learning methods: a cohort study

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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“…However, it did not encompass the general population, evaluating only patients referred for exercise testing, and focused mainly on factors related to cardiovascular diseases and exercise test results. Furthermore, Namatollahi et al [ 25 ] designed a predictive model for hypertension based on factors associated with body composition in a cross-sectional study utilizing data from the same adult cohort in Fasa. This study also followed a cross-sectional design, focusing exclusively on factors related to body structure.…”

“…In contrast, considering the follow-up phase of this cohort, we conducted the current longitudinal study, incorporating a broader range of factors. Most studies conducted in the field of ML models for predicting hypertension [ 8 , 26 – 28 ], including studies by AlKaabi and Namatollahi [ 1 , 25 ], were based on cross-sectional data. Firstly, cross-sectional studies cannot precisely determine the exact timing of future hypertension development in patients.…”

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“…Higher FATP and older age were directly associated with hypertension, while higher FFM and BMR were inversely related. The most accurate methods were AutoMLP, stacking, and voting, with accuracy rates of 90%, 84%, and 83%, respectively, indicating that BIA-derived body composition is a viable predictor of hypertension [ 8 ].…”

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“…Machine learning, an application of artificial intelligence technology, is a subset of statistics and computer science that allows for the processing of large amounts of data, and can be used to quickly and accurately define and quantify data from CT scans [16,26,33]. Similar technology has been used to predict other conditions such as hypertension in various populations [34]. This study expands on the growing body of literature investigating the use of machine learning technologies and opportunistic CTs in conjunction with clinical factors to predict outcomes in surgical, orthopedic, and oncologic populations.…”