HYBRID MACHINE LEARNING MODEL FOR ENHANCED CARDIOVASCULAR DISEASE PREDICTION

Abstract: The prediction of heart disease remains a critical challenge in healthcare, necessitating advanced computational methods to enhance diagnostic accuracy and patient outcomes. This study proposes a hybrid machine learning model integrating Convolutional Neural Networks (CNN) and extreme Gradient Boosting (XG-Boost) to improve heart disease prediction. The CNN component excels in automatically extracting complex features from diverse input data, including medical records, wearable device readings, and genomic information. These extracted features are then fed into the XG-Boost model, known for its robust classification capabilities, to accurately predict the presence or absence of heart disease.

Keywords: Hybrid machine learning, (CNN), (XG-BOOST), Data preprocessing, Performance Evaluation, Accuracy, Precision, Data privacy, Scalability, Gradient, boosting.