Analysis and Comparison of Kidney Stone Detection using Minimum Distance to Mean Classifier and Bayesian Classifier with Improved Classification Accuracy

Abstract

Aim: The goal of this research is to use minimum distance to mean classifier and bayesian classifiers to predict and detect kidney stones. Materials and Methods: This investigation made use of a collection of data from Kaggle website. Samples were collected (N=10) for normal kidney images and (N=10) for kidney with stone images. Total sample size was calculated using clinical.com. As a result the total number of samples 20 was considered for analysis. Using Matlab software and a standard data set collected from Kaggle website, the classification accuracy was obtained. Pretest G power taken as 85 in sample size calculation can be done through clinical.com. Results: The accuracy (%) of both classification techniques are compared using SPSS software by independent sample t-tests. There is a statistical significant difference between minimum distance to mean classifier and Bayesian classifier.Comparison results show that innovative minimum distance to mean classifier give better classification with an accuracy of (78.85%) than bayesian classifiers (71.1314%).There is a statistical significant difference between minimum distance to mean classifier and bayesian classifiers. The Minimum Distance to Mean classifier with p=0.708, p>0.05 insignificant and showed better results in comparison to Bayesian classifiers. Conclusion: The Minimum Distance to Mean Classifier appears to give better accuracy than the Bayesian Classifiers.

Imprint

Kishore U, Ramadevi R. Analysis and Comparison of Kidney Stone Detection using Minimum Distance to Mean Classifier and Bayesian Classifier with Improved Classification Accuracy. Cardiometry; Issue 25; December 2022; p.806-811; DOI: 10.18137/cardiometry.2022.25.806811; Available from: https://www.cardiometry.net/issues/no25-december-2022/minimum-distance-mean-classifier