Publication

Abstract : Every year one of the dominant reasons of global deaths is heart disease. Anticipation of these diseases in their early stages is pivotal and important in the healthcare sector, specifically in cardiology and angiology. To make predictive models to detect diseases in their early stages, machine learning plays a very important role. This paper presents various machine and deep learning models such as Support Vector Machines, Quadratic Discriminant Analysis, Multi-layer Perceptron Neural Networks, Ridge Classifier, and Extra trees classifiers on the real-time heart disease dataset. Additionally, this research utilized two types of evolution algorithms such as Differential Evolution and Particle Swarm Optimization algorithms to optimize the hyperparameters of considered models. Compared with the base models, the optimized models achieved better accuracy results.