Heart disease classification comparison among patients and normal subjects using machine learning and artificial neural network techniques

Abstract

Machine Learning (ML) and Artificial Neural Networks (ANN) have been successfully used for classifications in many of the prediction models. These algorithms provide good accuracy results in many of the applications like fall detection. 1 In this work, we compared the performance of ML and ANN on the Heart Disease data available from Cleveland database. 2 The data has 76 attributes, but we have considered 13 best features by doing correlation and selecting the features that has best correlation index to train our ML and ANN. There are 303 persons data, and we trained our algorithms with 80% of training data and 30% for testing. SVM showed 84% accuracy with sensitivity of 78.5% and specificity of 87.8% whereas ANN gives an accuracy of 87% with sensitivity of 85% and specificity of 88.2%. Overall, both ML and ANN give good accuracy results to distinguish people from with and without heart disease.