- Tytuł:
- Efficient heart disease diagnosis based on twin support vector machine
- Autorzy:
-
Brik, Youcef
Djerioui, Mohamed
Attallah, Bilal - Powiązania:
- https://bibliotekanauki.pl/articles/1840868.pdf
- Data publikacji:
- 2021
- Wydawca:
- Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
- Tematy:
-
heart diseases
medical data
diagnostic
machine learning
twin support vector machines
choroba serca
diagnostyka
uczenie maszynowe - Opis:
- 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.
- Źródło:
-
Diagnostyka; 2021, 22, 3; 3-11
1641-6414
2449-5220 - Pojawia się w:
- Diagnostyka
- Dostawca treści:
- Biblioteka Nauki
Artykuł