An Effective Approach to Detect Liver Disorder using KNN Algorithm in Comparison with Decision Tree Algorithm to Measure Accuracy

Aim: An effective approach to detect liver disorder using KNN algorithm in comparison with decision tree algorithm to measure accuracy. Methods and Materials: For identification of effective approaches to detect liver disorder, the KNN algorithm is used comparatively with the decision tree algorithm...

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Bibliographic Details
Published in:Cardiometry 2022-12 (25), p.1038-1046
Main Authors: Zaheer, M Mohammed, Nirmala, P
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Decision trees
Liver diseases
Online Access:Get full text
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Summary:Aim: An effective approach to detect liver disorder using KNN algorithm in comparison with decision tree algorithm to measure accuracy. Methods and Materials: For identification of effective approaches to detect liver disorder, the KNN algorithm is used comparatively with the decision tree algorithm which is an existing algorithm. The number of samples used in each group is 20. Result: It is discovered that the K- nearest neighbor (KNN) algorithm gives better mean accuracy than the decision tree algorithm. Each sample has a distinct level of accuracy, with KNN algorithm having a maximum accuracy value of 95.75%, which is higher than the decision tree algorithm 93.40%. The statistical results show that the KNN algorithm and decision tree algorithms have distinct statistical significance levels of p=0.001, i.e., p
ISSN:2304-7232
DOI:10.18137/cardiometry.2022.25/10381046