TY  - JOUR
T1  - Performance Analysis of Machine Learning Algorithms and Feature Selection Methods on Hepatitis Disease
AU  - Aydındağ Bayrak, Ebru
AU  - Kırcı, Pınar
AU  - Ensari, Tolga
PY  - 2019
DA  - December
JF  - International Journal of Multidisciplinary Studies and Innovative Technologies
JO  - IJMSIT
PB  - SET Teknoloji
WT  - DergiPark
SN  - 2602-4888
SP  - 135
EP  - 138
VL  - 3
IS  - 2
LA  - en
AB  - In this study, some machine learningclassification techniques are applied on Hepatitis data set acquired from UCIMachine Learning Repository. Naïve Bayes Classifier, Logistic Regression andJ48 Decision Tree are used as classification algorithms and they have beencompared according to filter-based feature selection methods. For filter-basedfeature selection, Cfs Subset Eval, Info Gain Attribute Eval and Principal Componentshave 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 dataset than the others with applied and non-applied filter-based featureselection. Moreover, we declare that the best filter-based feature selection isPrincipal Components because of the highest classification accuracy obtainedwith for hepatitis patients.
KW  - Hepatitis
KW  - Machine Learning
KW  - Feature Selection
KW  - Classification
KW  - Diagnosis
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UR  - https://dergipark.org.tr/en/pub/ijmsit/issue//641520
L1  - https://dergipark.org.tr/en/download/article-file/872391
ER  -