Cardiovascular Disease Prediction using AI And Streamlit Framework

This paper integrates both machine learning and deep learning techniques to predict cardiovascular disease, enhancing diagnostic accuracy through a dual approach. On the first side, Machine learning algorithms like Logistic Regression, Naïve Bayes and K-Nearest Neighbors (KNN) are used to analyze some of the structured patient data and lifestyle factors. On the second side, a Convolutional Neural Network (CNN) processes uploaded ECG images to predict disease. Both methods independently provide predictions on whether the patient is affected, combining insights from both structured data and image analysis. This approach not only improves prediction accuracy but also offers a robust tool for early detection and personalized treatment, assisting healthcare professionals and patients in making informed medical decisions.