COMPARISON OF DATA MINING CLASSIFICATION METHODS TO DETECT HEART DISEASE

  • Ira Ekanda Putri (1) Universitas Muhammadiyah Malang
  • Dwi Rahmawati (2*) Universitas Muhammadiyah Malang
  • Yufis Azhar (3) Universitas Muhammadiyah Malang

    • (*) Corresponding Author
DOI: https://doi.org/10.33480/pilar.v16i2.1388

Keywords: Heart Disease, Classification, Data Mining

Abstract

Heart disease is a disease that is deadly and must be treated as soon as possible because if it is too late, it has a big risk to one's life. Factors causing the disease of the heart is the use of tobacco, the physical who are less active, and an unhealthy diet. With existing data, the study is to compare the three algorithms, namely: Naive Bayes, Logistic Regression, and Support Vector Machine (SVM) which aims to determine the level of accuracy of the best of the dataset that is used to predict disease heart. This research produces the best accuracy of 87%, which is generated by the Naive Bayes method

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Published
2020-09-28
How to Cite
Putri, I., Rahmawati, D., & Azhar, Y. (2020). COMPARISON OF DATA MINING CLASSIFICATION METHODS TO DETECT HEART DISEASE. Jurnal Pilar Nusa Mandiri, 16(2), 213-218. https://doi.org/10.33480/pilar.v16i2.1388
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Vol 16 No 2 (2020): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Period for September 2020
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Copyright (c) 2020 Ira Ekanda Putri, Dwi Rahmawati, Yufis Azhar

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