Heart disease prediction by using novel optimization algorithm: A supervised learning prospective

Abstract

Data analysis in medicine is becoming more and more frequent to clarify diagnoses, refine research methods, and plan appropriate equipment supplies according to the importance of the pathologies that appear. Artificial intelligence offers software solutions that are required to analyze the present data for optimal prediction of results. A system model is capable of several data processing algorithms for the classification of heart disease. This research work is particularly interested in the category of data. The classification allows us to obtain a prediction model from training data and test data. These data are screened by a classification algorithm that produces a new model capable of detailed data, possibly having the same classes of data through the combination of mathematical tools and computer methods. To analyze the present data to predict optimal results, we need to use the optimization technique. This research work aims to design a framework for heart disease prediction by using major risk factors based on different classifier algorithms such as Naïve Bayes (NB), Bayesian Optimized Support Vector Machine (BO-SVM), K-Nearest Neighbors (KNN), and Salp Swarm Optimized Neural Network (SSA-NN). This research is carried out for the effective diagnosis of heart disease using the heart disease dataset available on the UCI Machine Repository. The highest performance was obtained using BO-SVM (accuracy = 93.3%, precision = 100%, sensitivity = 80%) followed by SSA-NN with (accuracy = 86.7%, precision = 100%, sensitivity = 60%) respectively. The results reveal that the proposed novel optimized algorithm can provide an effective healthcare monitoring system for the early prediction of heart disease.