Performance Comparison of Three Classification Algorithms for Non-alcoholic Fatty Liver Disease Patients Using Data Mining Tool

Adi Octaviantara; Moch Anwar Abbas; Ahmad Azhari; Dwiza Riana; Alya Shafira Hewiz

doi:10.37034/medinftech.v1i1.2

Authors

Adi Octaviantara Universitas Nusa Mandiri Jakarta
Moch Anwar Abbas Universitas Nusa Mandiri Jakarta
Ahmad Azhari Universitas Nusa Mandiri Jakarta
Dwiza Riana Universitas Nusa Mandiri Jakarta
Alya Shafira Hewiz Universitas Airlangga, Surabaya

DOI:

https://doi.org/10.37034/medinftech.v1i1.2

Keywords:

Performance, Comparison, Classification, Algorithms, Liver

Abstract

This study aims to carry out a comparative analysis of the three classification algorithms used in research on Nonalcoholic Fatty Liver Disease (NAFLD) Patients. NAFLD is a liver condition associated with the accumulation of fat in the liver in individuals who do not consume excessive alcohol. The algorithms used in the analysis are Decision Tree, Naïve Bayes, and k-Nearest Neighbor (k-NN), with data processing using RapidMiner software. The data used is sourced from Kaggle which comes from the Rochester Epidemiology Project (REP) database with research conducted in Olmsted, Minnesota, United States. The measurement results show that the Decision Tree algorithm has an accuracy of 92.56%, a precision of 93.24%, and a recall of 99.08%. The Naïve Bayes algorithm has an accuracy of 89.93%, a precision of 95.40% and a recall of 93.56%. While the k-Nearest Neighbor algorithm has an accuracy of 91.33%, a precision of 91.94%, and a recall of 99.27%. ROC curve analysis, all algorithms show "Excellent" classification quality. However, only the k-NN algorithm reached 1.0, showing excellent classification results in solving the problem of classifying Nonalcoholic Fatty Liver Disease patients. This study concluded that the k-NN algorithm is a better choice in solving the problem of classifying Non-alcoholic Fatty Liver Disease patients compared to the Decision Tree and Naïve Bayes algorithms. This study provides valuable insights in the development of classification methods for the early diagnosis and management of NAFLD.

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