Comparison of Machine Learning Classification Methods in Hepatitis C Virus

Authors

  • Lailis Syafa’ah Department of Electrical Engineering, Universitas Muhammadiyah Malang, Indonesia, Indonesia
  • Zulfatman Zulfatman Department of Electrical Engineering, Universitas Muhammadiyah Malang, Indonesia, Indonesia
  • Ilham Pakaya Department of Electrical Engineering, Universitas Muhammadiyah Malang, Indonesia, Indonesia
  • Merinda Lestandy Department of D3 Electronics Technology, Universitas Muhammadiyah Malang, Indonesia, Indonesia

DOI:

https://doi.org/10.15575/join.v6i1.719

Keywords:

HCV, KNN, Naïve Bayes, Neural network, Random forest, Classification, Machine learning

Abstract

The hepatitis C virus (HCV) is considered a problem to the health of societies are the main. There are around 120-130 million or 3% of the world's total population infected with HCV. Without treatment, most major infectious acute evolve into chronic, followed by diseases liver, such as cirrhosis and cancer liver. The data parameters used in this study included albumin (ALB), bilirubin (BIL), choline esterase (CHE), -glutamyl-transferase (GGT), aspartate amino-transferase (AST), alanine amino-transferase (ALT), cholesterol (CHOL), creatinine (CREA), protein (PROT), and Alkaline phosphatase (ALP). This research proposes a methodology based on machine learning classification methods including k-nearest neighbors, naïve Bayes, neural network, and random forest. The aim of this study is to assess and evaluate the level of accuracy using the algorithm classification machine learning to detect the disease HCV. The result show that the accuracy of the method NN has a value of accuracy are high, namely at 95.12% compared to the method KNN, naïve Bayes and RF in a row amounted to 89.43%, 90.24%, and 94.31%.

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Published

2021-06-17

Issue

Vol. 6 No. 1 (2021)

Section

Article

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