Design and Implementation of a Hybrid Machine Learning Model for Enhanced Heart Disease Prediction Using Patient Health Data

Heart disease remains one of the leading causes of mortality worldwide, creating a significant burden on healthcare systems and society. Early and accurate prediction of heart disease is essential for reducing mortality rates and improving patient outcomes. Traditional diagnostic methods often rely on clinical expertise and extensive medical examinations, which can be time-consuming and prone to human error. Recent advancements in machine learning have enabled the development of intelligent prediction systems capable of analyzing large volumes of patient health data and identifying disease patterns with high accuracy. This study proposes and implements a hybrid machine learning model that combines multiple classification techniques to enhance the prediction accuracy of heart disease. The proposed framework integrates the strengths of various machine learning algorithms, including Decision Tree, Random Forest, and Support Vector Machine, to overcome the limitations of individual models. Patient health attributes such as age, blood pressure, cholesterol level, heart rate, and other clinical indicators are utilized for model training and evaluation. Data preprocessing techniques including normalization, feature selection, and missing value handling are applied to improve data quality and model performance. Experimental results demonstrate that the hybrid model achieves superior accuracy, precision, recall, and F1-score compared to standalone classifiers. The findings highlight the effectiveness of ensemble-based learning approaches in supporting healthcare professionals with reliable decision-making tools for early heart disease detection and risk assessment.