Cardiovascular Disease Prediction Using Deep Learning Techniques

This research focuses on developing a cardiovascular disease prediction system using deep learning and machine learning techniques applied to a structured CSV dataset containing patient health parameters. The system classifies cardiovascular risk into five categories—0: Normal, 1: Mild, 2: Moderate, 3: Severe, and 4: Very Severe—allowing for early diagnosis and timely intervention. Multiple algorithms, including SVM, Random Forest, KNN, Decision Tree, and ANN, are implemented and compared to determine the most accurate predictive model. The backend is built using Python with the Flask framework, while the frontend is designed with HTML, CSS, and JavaScript to provide a smooth and interactive user interface. Data preprocessing steps such as cleaning, normalization, and feature selection are applied to ensure optimal model accuracy. The system evaluates model performance using accuracy, precision, recall, and F1-score, delivering a reliable and efficient platform for cardiovascular risk assessment that supports both healthcare professionals and individuals in informed preventive healthcare decision-making.