EFFICIENT HEART DISEASE DIAGNOSIS BASED ON TWIN SUPPORT VECTOR MACHINE

dc.contributor.authorYoucef BRIK
dc.contributor.authorMohamed DJERIOUI
dc.contributor.authorBilal ATTALLAH
dc.date.accessioned2021-07-26T09:51:14Z
dc.date.available2021-07-26T09:51:14Z
dc.date.issued2021
dc.description.abstractHeart 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 hyper-plane 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 accuracyen_US
dc.identifier.urihttp://dspace.univ-msila.dz:8080//xmlui/handle/123456789/25167
dc.publisherUniversité de M'silaen_US
dc.subjectHeart diseases, medical data, diagnostic, machine learning, twin support vector machinesen_US
dc.titleEFFICIENT HEART DISEASE DIAGNOSIS BASED ON TWIN SUPPORT VECTOR MACHINEen_US
dc.typeArticleen_US

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