Hybrid Machine Learning Model for Hypertension Detection

Abstract: Hypertension, a leading risk factor for cardiovascular diseases, requires early detection to prevent severe health complications. This paper presents a hybrid machine learning model integrating Random Forest, XGBoost, Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and Logistic Regression using a voting-based ensemble method. The dataset is pre-processed with SMOTE to handle class imbalance, and features are normalized for optimal performance. The proposed model achieves an accuracy of 89%, outperforming individual classifiers. The results indicate that ensemble learning significantly enhances prediction reliability.

Keywords: Hypertension, Machine Learning, Hybrid Model, Ensemble Learning, SMOTE