AI Innovations in Heart Disease Prediction using Machine Learning and Risk Stratification using Deep Learning

The goal of the heart disease prediction project is to use machine learning and deep learning techniques to create an accurate and efficient system. To obtain insights into the dataset, the project starts with thorough data preprocessing, which includes data cleansing, addressing missing values, and conducting exploratory data analysis. Data visualisation tools are used to support this research in order to find important patterns and correlations that help with the model-building stage. By combining several decision trees, the method increases the predictive potential of the model. To guarantee robustness and dependability, performance is assessed using important measures like accuracy, precision, recall, and cross-validation. A deep learning algorithm is also included to manage data on heart illness that is based on images. Convolutional neural networks (CNNs) use the Lenet architecture for image classification, and pre-processing and data augmentation approaches improve model performance. In comparison to conventional approaches, this work suggests a sophisticated heart disease prediction model that combines Machine Learning (ML) and Deep Learning (DL) techniques, attaining an accuracy of X% and greatly lowering misclassification rates. By employing oversampling approaches, the suggested system effectively manages unbalanced datasets and enhances prediction robustness through feature selection and ensemble learning.