A Fact-Based Liver Disease Prediction by Enforcing Machine Learning Algorithms

Ram, Mylavarapu Kalyan; Sujana, Challapalli; Srinivas, Rayudu; Murthy, G. S. N.

doi:10.1007/978-981-33-6862-0_45

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…In contrast, the SPSS Modeler yielded an accuracy of 74.26% and a precision of 40.24%. Similarly, Ram et al [19] conducted analyses using Python to predict liver diseases, employing methods such as Naive Bayes (NB), SMO, and Bayes Net. On the other hand, Alam, Rahman, and Rahman [49] used the Weka software in their study, applying the Bayes Net method.…”

Section: Resultsmentioning

confidence: 99%

“…The present study employs the Naive Bayes method from data mining techniques for the prediction of liver diseases. A review of the literature reveals numerous studies that have utilized this method for the same purpose [12,[16][17][18][19][20]. Unlike other studies, this research also evaluates the classification performance of the Naive Bayes method when applied with different Cross-Validation techniques.…”

Section: Introductionmentioning

confidence: 99%

“…Numerous studies have applied various data mining methods to data related to liver diseases. Ram et al [19] used Naive Bayes (NB), SMO, and Bayes Net methods in their study to predict liver diseases, noting that the SMO method displayed superior performance. Vijayarani and Dhayanand [21] implemented NB and Support Vector Machine (SVM) methods in their research, indicating that the SVM method performed better.…”

Section: Introductionmentioning

confidence: 99%

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Classification of Liver Disorders Diagnosis using Naïve Bayes Method

Bezek Güre

2024

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

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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.

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Section: Resultsmentioning

confidence: 99%