Deep learning-based cardiovascular disease prediction system

Abstract

This study explores the use of deep learning techniques to enhance the prediction of cardiovascular disease. Traditional models, such as Support Vector Machines, often fail to capture complex patterns in medical data. To address this, we evaluate Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and hybrid models that incorporate Genetic Algorithms and Fuzzy Logic. The Cardiovascular Disease dataset is preprocessed and subjected to feature engineering and hyperparameter tuning to improve model performance. Among the models tested, CNN achieved the highest accuracy, outperforming traditional approaches. The hybrid models show promise in further increasing prediction accuracy. This research highlights the potential of AI-based diagnostic tools in improving early detection and treatment planning for cardiovascular conditions.