Effective Classification of Heart Disease Using Convolutional Neural Networks

Accurate classification of heart disease is crucial for precise diagnosis, requiring the disease condition to be predicted with high accuracy. Deep learning-based diagnostic algorithms are developed to learn from and predict disease status using Electrocardiogram signals derived from extensive pathology data. This method can potentially save more lives. Utilizing a large number of Electronic Health Records, this research aims to explore the benefits of deep learning methods and Sand Cat Swarm Optimization (SCSO) for feature selection in predicting patient disease. A dataset containing pathogenic information for patients with various diseases is pre-processed, and the SCSO approach is used to choose the most important features for determining disease severity. A Convolutional Neural Network is used for classification in conjunction with Principal Component Analysis, a Restricted Boltzmann Machine, and deep convolutional Generative Adversarial Network models. These models are developed using the selected features for learning intricate correlations between the input features and the disease state. The effectiveness of methods is assessed with the help of parameters such like accuracy, precision, recall, and F1-score. The proposed model also offers additional benefits for heart disease prognosis, improving accuracy and reliability of disease prediction.