Detecting heart diseases through machine learning

Abstract

Heart diseases are ranked the first cause of death in the world. Australia has the highest incidence of heart disease. Approximately 125 lives every single day that's one life every 12 minutes. Heart disease describes a range of conditions that affect the heart or blood vessels and can affect anyone at any age. Also, a major concern, heart disease could cause a heart attack or stroke. Some symptoms may include chest pain, shortness of death, dizziness, fatigue, or nausea. Other serious symptoms, such as diabetes and high cholesterol, may lead to heart attacks. A healthy lifestyle, quitting smoking, and exercising are small steps to avoid heart disease. Heart diseases are easier to treat when detected early. In this paper, an effective heart disease framework is proposed. Support Vector Machine (SVM), Multilayer Perceptron (MLP), Random Forest (RF), and Radial Basic Function (RBF) techniques for classification are used. Moreover, Feature selection is performed to minimize the features to have better accuracy. Info Gain Attribute Eval -Ranker algorithm is used for feature selection. In addition, classification techniques and feature selection algorithms are applied to the LIAC heart stat log dataset which depends on the heart diseases dataset. The result's effectiveness is described by accuracy, precision, recall, and ROC Curve.