An Efficient Approach to Detect Liver Disorder Using Naive Bayes in Comparison with Decision Tree Algorithm to Measure Accuracy

Abstract

Aim: To process an effective approach to detect liver disorder using Naive Bayes algorithm in comparison with Decision tree algorithm to measure accuracy. Methods & Materials: There are 20 samples used for both groups, where group 1 is Naive Bayes algorithm and group 2 is Decision tree algorithm which are effectively used for the identification of liver disorder approach. Result: It is a novel detection method, and it has been discovered that the Naive Bayes algorithm performs better than the Decision tree algorithm. Each sample has a distinct level of accuracy, with Naive Bayes having a mean accuracy of 94.80%, which is higher than the decision tree algorithm 92.20%. The statistical results show that the Naive Bayes and Decision tree algorithms have different statistical significance levels of p=0.01, i.e., p<.0.05 (independent sample T-test). Conclusion: On the basis of a liver disorder analysis, it is obvious that the Naive Bayes algorithm has a higher mean accuracy value than the Decision tree method. The Naive Bayes algorithm has a 94.80% accuracy rating, while decision tree has 92.20% rating.

Imprint

M. Mohammed Zaheer, P. Nirmala. An Efficient Approach to Detect Liver Disorder Using Naive Bayes in Comparison with Decision Tree Algorithm to Measure Accuracy. Cardiometry; Issue 25; December 2022; p.1047-1053; DOI: 10.18137/cardiometry.2022.25.10471053; Available from: https://www.cardiometry.net/issues/no25-december-2022/disorder-using-naive-bayes