Soft Computing Approaches for Medical Diagnosis and Prognosis of Heart Disease with Feature Analysis

In the field of medical diagnosis and prognosis, particularly for heart disease, deep learning techniques, specifically those utilizing convolutional neural networks (CNNs), have shown significant promise. With a focus on feature analysis to improve accuracy and interpretability, this abstract provides an overview of deep learning models using CNNs for the study of heart disease. This study aims to build reliable CNN-based models that can precisely diagnose and forecast the prognosis of heart disease using medical data. Large datasets containing various cardiac data, including patient demographics, medical imaging scans, and electrocardiograms (ECGs), are used to train the models to accomplish. The proposed deep learning methods heavily rely on feature analysis. The most instructive and pertinent features from the input data are extracted using feature extraction techniques, and these features are then fed into the CNN models. The CNN models are trained to learn complicated patterns and correlations contained in the data, enabling precise categorization and prediction of cardiac disease. Additionally, feature analysis improves the CNN models' interpretability. To comprehend the areas and aspects of the input data that contribute the most to the model's decision-making process, methods including saliency maps, gradient-based visualization, and class activation mapping are used. This improves clinical decision-making by giving doctors and researchers important insights into the diagnostic and predictive variables of cardiac disease.