Optimization using Internet of Agent based Stacked Sparse Autoencoder Model for Heart Disease Prediction

Abstract

Recently, machine learning methods have been successfully used for the prediction of cardiovascular disease. Early diagnosis and prediction is necessary for giving effec tive treatment to avoid higher mortality rates. Several classification algorithms have been developed recently which satisfy the need, but show limited accuracy while predicting the heart disease. Hence, the focus of this study is on early prediction of heart disease and to improve the accuracy of prediction using benchmark heart dis ease datasets such as UCI Cleveland dataset and Heart disease clinical dataset by implementing effective classification and optimization algorithms. Optimization algo rithms generally exhibit the benefit of dealing with complex non-linear issues with better adaptability and flexibility. The Emperor penguin optimization algorithm, which can select the best features for classification has been utilized in this study to improve the efficiency, minimize reconstruction errors, and increase the quality of heart disease classification. Further, the newly developed stacked sparse con volutional neural network based auto encoder (SSC-AE) classification algorithm has been employed for significant feature classification with higher robustness and effi cacy. Accuracy, Area Under Curve (AUC), and F1 score are some of the measures used to compare the outcomes of several machine learning algorithms to those of the proposed model in this study. The results show that the proposed model, SSC AE, is superior to other classification models.