A Hybrid Machine Learning and Deep Learning Model for Precise Cardiovascular Disease Prediction

Cardiovascular disease (CVD) remains one of the leading causes of death globally, posing a significant challenge to healthcare systems. Early and accurate prediction of CVD is crucial to reduce its impact and improve patient outcomes. This paper presents a hybrid model combining machine learning (ML) and deep learning (DL) techniques for precise prediction of cardiovascular disease. We utilized two public heart disease datasets with 70,000 and 1,190 records, along with a locally collected dataset containing 600 records. Our model incorporates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks as the deep learning components, and K-Nearest Neighbors (KNN) and XGBoost (XGB) as the machine learning components. Majority voting was employed as an ensemble method to combine the outputs of the classifiers, producing the final prediction. Experimental results show that the proposed model achieved superior classification performance across all evaluation metrics, demonstrating its effectiveness and reliability for forecasting cardiovascular disease.