Heart Disease Prediction Using Artificial Neural Networks

Heart disease is a prominent cause of death worldwide, underlining the crucial need for effective diagnostic techniques that can deliver precise and timely predictions. In this paper, we deploy a complex Artificial Neural Network (ANN) model to offer a deep learning framework for the prediction of cardiac illness. To achieve optimal performance, the proposed model integrates a variety of critical data preparation techniques, such as feature scaling to normalise the input data and label encoding for categorical variables. To decrease overfitting and increase generalisation, our model design contains several hidden layers, batch normalisation, and dropout regularisation. Using a common dataset, we analyse the model and found that it uses rigorous training and validation procedures to attain excellent accuracy. The findings indicate how resilient our strategy is, as the model not only achieves good classification accuracy but also decreases validation loss. This work presents a scalable, automated, and exceptionally reliable method for early heart illness diagnosis, representing a significant development in the use of deep learning-based solutions in predictive healthcare. The discoveries have the potential to be employed in actual clinical settings, offering medical practitioners access to a cutting-edge decision support tool that can help with early heart disease detection and prevention.