Leveraging Bayesian Optimization in Deep Learning for Accurate Heart Disease Prediction

The accurate prediction of heart disease is a significant challenge in medical diagnostics, directly impacting patient care and treatment decisions. This project introduces an innovative approach to heart disease prediction by leveraging Bayesian Optimization in deep learning models. By utilizing Bayesian optimization for hyperparameter tuning, the model efficiently identifies the optimal configuration for deep neural networks, resulting in improved predictive accuracy. The deep learning architecture is designed to learn complex patterns in the data, and Bayesian optimization is employed to optimize key hyperparameters such as learning rate, batch size, and the number of hidden layers. The model is evaluated using standard metrics like accuracy, precision, recall, and F1-score. The results demonstrate that the Bayesian-optimized deep learning model significantly outperforms traditional machine learning algorithms, such as Logistic Regression (LR), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM), in predicting heart disease with higher accuracy and reliability. This project highlights the effectiveness of combining Bayesian optimization with deep learning to enhance predictive performance, offering a promising solution for automated medical diagnostics and improving clinical decision-making in the detection of heart disease. Keywords : Optimiztation, CNN, Ensemble, Bayesian