Classification of Liver Disorders Diagnosis using Naïve Bayes Method

Year 2024, , 153 - 160, 24.03.2024

Özlem Bezek Güre

https://doi.org/10.17798/bitlisfen.1361016

Abstract

Liver diseases pose a significant health challenge, necessitating robust predictive tools for early diagnosis. This study aims to determine the predictive performance of Naive Bayes classifier, one of the data mining algorithms, in the classification of liver diseases. The study applied 5, 10 and 20-fold cross-validation method. Trying to determine the effect of the cross-validation (CV) method used on the classification performance, this study used the "BUPA" dataset in the UCI Machine Learning Repository database for this purpose. The dataset consists of 6 variables and 345 examples. Orange program was used for data analysis. The study showed that the accuracy of the Naive bayes method were 64.6%, 66.7% and 64.3%, respectively. Accordingly, it can be said that the 10-fold CV method performs better. Compared to similar studies, it can be claimed that the analysis results obtained with the Orange program are better.

Keywords

Data mining, machine learning, Naive Bayes, liver diseases

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