Hypertension Classification Using Machine Learning Part II

High blood pressure (BP) or hypertension is a dangerous and deadly condition which can lead to serious disorders and high risk of heart attacks, strokes or death. Therefore, studying and monitoring blood pressure levels is highly important. In this study, we propose four distinct machine learning classification models to predict blood pressure levels. The classifiers used are: Random Forest (RF), CatBoost (CB), Support Vector Machine (SVM), and, K-Nearest Neighbors (KNN). Furthermore, several performance indicators such as accuracy, specificity, precision, recall, and F1 score have been calculated for each model. An accuracy of up to 90% was achieved for CATBoost and RF, and up to 87% and 78.33% for SVM and KNN respectively. This study was able to predict blood pressure-related disorders and cardiovascular diseases.