An Efficient Machine Learning Based Model To Predict Heart Disease

Across the globe, cardiovascular diseases remain a leading contributor to death rates and accurate prediction are essential to modern health systems. Unhealthy lifestyles are one of the elements leading to the increasing occurrence of heart disease, stress and aging, and it has become essential to create a system capable of delivering precise and reliable results diagnosis. With the growing accessibility of vast healthcare data, machine learning technology is emerging as an important tool for helping clinical decision-making by identifying hidden patterns and relationships in complex data sets. In this study, we developed a machine learning-based system for predicting heart disease. The proposed system uses a structured set of data obtained from a publicly available UCI source, contain important medical parameters. To guarantee high data quality and raise the level of performance of models, multiple preprocessing techniques were implemented, including data cleaning, feature normalization, and handling of missing values, classification variable encoding and outlier detection. Different approaches were tested to identify the most effective model. The models were evaluated based on performance indicators such as recall, accuracy, and precision and ROC-AUC points. The study focuses on the performance of ensemble learning using Random Forest, while comparative analysis shows that KNN achieved slightly higher accuracy on the given dataset. K-Nearest Neighbors performed the best, achieving an accuracy of around 91.8% and superior classification capabilities indicated by ROC curves and overall evaluation metrics. Our proposed approach can be used as an effective decision-making tool for medical professionals to identify high-risk patients in time. Finally, this approach helps reduce mortality rates and can assist doctors in early detection and better decision-making.