UCLA

High blood pressure is a prevalent health concern worldwide, and identifying the factors that contribute to its development is crucial for prevention and management strategies. This study aimed to investigate the influence of sex, hereditary factors, habitats, and BMI on the risk of high blood pressure using machine learning techniques. Several models, including Logistic Regression, Decision Trees, Random Forests, XGBoost, Support Vector Machines, and Neural Networks are employed on the public-use sample from the Add Health dataset.