Efficient heart disease diagnosis based on twin support vector machine

Abstract

Heart disease is the leading cause of death in the world according to the World Health Organization (WHO). Researchers are more interested in using machine learning techniques to help medical staff diagnose or detect heart disease early. In this paper, we propose an efficient medical decision support system based on twin support vector machines (Twin-SVM) for heart disease diagnosing with binary target (i.e. presence or absence of disease). Unlike conventional support vector machines (SVM) that finds only one optimal hyperplane for separating the data points of first class from those of second class, which causes inaccurate decision, Twin-SVM finds two non-parallel hyper-planes so that each one is closer to the first class and is as far from the second class as possible. Our experiments are conducted on real heart disease dataset and many evaluation metrics have been considered to evaluate the performance of the proposed method. Furthermore, a comparison between the proposed method and several well-known classifiers as well as the state-of-the-art methods has been performed. The obtained results proved that our proposed method based on Twin-SVM technique gives promising performances better than the state-of-the-art. This improvement can seriously reduce time, materials, and labor in healthcare services while increasing the final decision accuracy.

RideNN‐OptDRN: Heart disease detection using RideNN based feature fusion and optimized deep residual network

“…A random forest algorithm model combined with flask framework and hyper text markup language (HTML) front end technology for detecting the heart disease modeled in Reference 21 offered optimal predicted values with improved accuracy. However, this method was not suitable for real‐time implementation and the users cannot able to upload their test results as images. The more common challenge related to heart disease prediction is the overfitting problem lies in the dataset. In Reference 22, the designed method failed to utilize robust algorithms for choosing the most pertinent features for finding which one was more appropriate for prediction needs, and also this method was very ineffective in providing life‐saving and economic healthcare services.…”