Enhancing Heart Disease Prediction: A Novel Deep Learning Approach with Artificial Neural Networks

The crucial role of the heart in all living beings necessitates a high degree of accuracy in diagnosing and predicting heart disease. Even minor errors can lead to serious consequences, highlighting the importance of developing efficient methods for heart disease prediction. Deep learning techniques offer a promising approach in this regard. Early identification of cardiac disease markers is vital for clinical decision-making and paves way for further diagnostic evaluation. However, traditional manual analysis is time-consuming and laborious, especially when dealing with large datasets. Here, this study proposes a novel model using deep learning techniques to achieve rapid and effective heart disease diagnosis based on various physiological markers. The proposed model demonstrates exceptional performance on both training and testing data. We employ Artificial Neural Networks (ANN) to assess the model's efficacy in accurately predicting the presence or absence of heart disease. The proposed approach offers several advantages, including high success rates, superior performance and accuracy compared to traditional methods like Random Forest and SVM, flexibility, and adaptability.