Performance Analysis of Machine Learning Algorithms and Feature Selection Methods on Hepatitis Disease

Year 2019, Volume: 3 Issue: 2, 135 - 138, 23.12.2019

Ebru Aydındağ Bayrak Pınar Kırcı Tolga Ensari

Abstract

In this study, some machine learning
classification techniques are applied on Hepatitis data set acquired from UCI
Machine Learning Repository. Naïve Bayes Classifier, Logistic Regression and
J48 Decision Tree are used as classification algorithms and they have been
compared according to filter-based feature selection methods. For filter-based
feature selection, Cfs Subset Eval, Info Gain Attribute Eval and Principal Components
have been used and the performance of them is evaluated in terms of precision,
recall, F-Measure and ROC Area. Among the all used classification algorithms,
Naïve Bayes Classifier has higher classification accuracy on the Hepatitis data
set than the others with applied and non-applied filter-based feature
selection. Moreover, we declare that the best filter-based feature selection is
Principal Components because of the highest classification accuracy obtained
with for hepatitis patients.    

Keywords

Hepatitis, Machine Learning, Feature Selection, Classification, Diagnosis

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