HEART DISEASE PREDICTION USING MACHINE LEARNING

Cardiovascular disease remains one of the leading causes of mortality worldwide, posing a serious threat to global public health. Early identification of heart disease can significantly reduce fatality rates and improve patient outcomes. However, accurate diagnosis using conventional clinical analysis is a challenging and time-consuming task. Recent advancements in machine learning (ML) have provided promising solutions for intelligent medical decision-making and predictive analytics. This study proposes a hybrid machine learning framework for the early prediction of heart disease using the Cleveland Heart Disease dataset. The proposed system integrates data mining techniques with supervised learning algorithms to enhance diagnostic accuracy. Three models were implemented and evaluated, including Decision Tree, Random Forest, and a novel Hybrid model combining both approaches. The hybrid model leverages the interpretability of Decision Trees and the robustness of Random Forest to improve predictive performance. Experimental results demonstrate that the proposed hybrid approach achieves an accuracy of 88.7%, outperforming the individual models. Additionally, a user-friendly interface was developed to collect patient clinical parameters and provide real-time heart disease prediction. The proposed framework can assist healthcare professionals in early diagnosis and clinical decision support, thereby reducing the risk of severe cardiovascular events.