Modeling Health Risk Probabilities of Obesity and Hypertension Using Machine Learning

Abstract

This study investigates the use of machine learning techniques to model health risk probabilities using data from the National Health and Nutrition Examination Survey 2017–2018. The objective is to evaluate the predictive performance of multiple models across health outcomes, including hypertension, stroke, kidney disease, and sleep disorders, with a focus on the relationship between obesity and hypertension. Variables such as body mass index, waist circumference, age, gender, and blood pressure measurements were used to develop predictive models. The results indicate that hypertension prediction achieved the highest performance, while stroke and kidney disease showed moderate accuracy. Sleep-related outcomes showed reduced predictive power due to limited feature representation. Additionally, the analysis confirmed a strong relationship between obesity and hypertension, as individuals with higher body mass index exhibited increased blood pressure levels. Overall, the findings highlight the potential of machine learning in health risk prediction while emphasizing the importance of data quality and feature selection.