Efficient heart disease diagnosis based on twin support vector machine

• Autorzy: Brik Youcef , Djerioui Mohamed , Attallah Bilal

Identyfikatory

DOI

10.29354/diag/139241

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

heart diseases; medical data; diagnostic; machine learning; twin support vector machines

PL

choroba serca; diagnostyka; uczenie maszynowe

• Wydawca: Polskie Towarzystwo Diagnostyki Technicznej
• Czasopismo: Diagnostyka
• Rocznik: 2021
• Tom: Vol. 22, No. 3
• Strony: 3--11
• Opis fizyczny: Bibliogr. 34 poz., rys., tab.

Twórcy

autor

Brik Youcef

• LASS Laboratory, Faculty of technology, University Mohamed Boudiaf of M’sila, Algeria

autor

Djerioui Mohamed

• LASS Laboratory, Faculty of technology, University Mohamed Boudiaf of M’sila, Algeria

autor

Attallah Bilal

• LASS Laboratory, Faculty of technology, University Mohamed Boudiaf of M’sila, Algeria

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).