# Analysis and Comparison of Kidney Stone Detection using Minimum Distance to Mean and Gaussian Maximum Likelihood Classifier with Improved Classification Accuracy

## Abstract

Aim: The goal of this research is to use Gaussian maximum Likelihood classifier and Minimum distance to mean Classifier 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 80, sample size calculation can be done through clinicalc.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 Gaussian maximum Likelihood classifier andMinimum distance to mean Classifier. Comparison results show that innovative Gaussian maximum Likelihood classifiers give better classification with an accuracy of (81.34%) than Minimum distance to mean Classifiers (78.85%).There is a statistical significant difference between Gaussian maximum Likelihood classifier and Minimum distance to mean Classifiers. The Gaussian maximum Likelihood classifier with p=0.022, p<0.05 hence significant and showed better results in comparison to Minimum distance to mean classifiers. Conclusion: The Gaussian maximum likelihood classifier appears to give better classification accuracy than the Minimum distance to mean Classifier.

## Imprint

Kishore U, Ramadevi R. Analysis and Comparison of Kidney Stone Detection using Minimum Distance to Mean and Gaussian Maximum Likelihood Classifier with Improved Classification Accuracy. Cardiometry; Issue 25; December 2022; p.812-817; DOI: 10.18137/cardiometry.2022.25.812817; Available from: https://www.cardiometry.net/issues/no25-december-2022/classifier-improved-classification-accuracy