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Original research

Analysis and Comparison of Kidney Stone Detection using Parallel Piped Classifier and Bayesian Classifier with Improved Classification Accuracy

Abstract

Aim: The goal of this research is to use parallel piped classifiers 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 considered as (N=10) for parallel piped classifiers and (N=10) for bayesian classifiers according to clinicalc.com, total sample size calculated. The accuracy was calculated by using MATLAB with a standard data set. 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 significant difference between the two classification techniques. Comparison results show that innovative parallel piped classifiers give better classification with an accuracy of (83.5410%) than bayesian classifiers (71.1314%).There is a statistical significant difference between parallelepiped classifiers and bayesian classifiers. The parallel piped classifiers with p=0.007, p<0.05 significant accuracy(83.54%) showed better results in comparison to bayesian classifiers. Conclusion: The parallel piped classifiers appear to give better classification accuracy than the bayesian classifiers.

Imprint

Kishore U, Ramadevi R. Analysis and Comparison of Kidney Stone Detection using Parallel Piped Classifier and Bayesian Classifier with Improved Classification Accuracy. Cardiometry; Issue 25; December 2022; p.794-798; DOI: 10.18137/cardiometry.2022.25.794798; Available from: https://www.cardiometry.net/issues/no25-december-2022/analysis-comparison-kidney

Keywords

Kidney Stone,  Image Classification,  Classifier,  Innovative Parallel Piped Classifier,  Bayesian Classifier,  Machine Learning
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