A Comparative Performance Analysis of Classification Algorithms for Hypertension Diagnosis

Abstract

Hypertension is a leading cause of cardiovascular diseases, strokes, and kidney failure, with early diagnosis being critical for prevention. Traditional diagnostic methods often face challenges such as human error and inconsistent measurements. While machine learning (ML) has been explored as a potential solution, previous studies have mainly focused on accuracy, often neglecting other important metrics like precision, recall, and F1-score, especially in imbalanced datasets. The primary purpose of this research is to address this gap by comprehensively comparing the performance of four machine learning algorithms - Naive Bayes, Support Vector Machines (SVM), Random Forest (RF), and XGBoost—to provide valuable insights for practical hypertension screening. The dataset consists of 1,985 records with 10 predictor features, including both categorical and continuous variables, and a binary target variable (Has_Hypertension: Yes/No) with a class distribution of 1,032 Yes and 953 No. The data undergoes preprocessing, including categorical encoding and feature scaling for SVM. Models are evaluated using a balanced set of metrics, including accuracy, precision, recall, and F1-score. The results show that RF/XGBoost perform best, with the highest F1 and accuracy, while SVM and Naive Bayes serve as competitive alternatives.