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Original research

Comparative analysis of chronic kidney disease using novel decision tree algorithm by comparing linear regression for obtaining better accuracy

Abstract

Aim: Chronic kidney disease (CKD) is among the main 20 reasons for death worldwide and influences around 10% of the world grown-up populace. CKD is an issue that upsets typical kidney work. The main objective of this study aims to find the best-suited algorithm that will give us the most ideal prediction. We will be comparing Novel Decision Tree with Linear Regression to find out which of these can give us the best accuracy. Material and Methods: The study used 322 samples with Novel Decision Tree and Linear Regression is executed with varying training and testing splits for predicting the accuracy for kidney disease prediction with the G-power value of 80% and the kidney datasets were collected from various web sources with recent study findings and threshold 0.05%, confidence interval 95% mean and standard deviation. The performance of the classifiers are evaluated based on their accuracy rate using the chronic kidney disease dataset. Results: The accuracy of predicting kidney disease in Novel Decision Tree (96.66%) and Linear Regression (85.25%) is obtained. There is a statistical 2-tailed significant difference in accuracy for two algorithms is 0.000 (p<0.05) by performing independent samples t-tests. Conclusion: This study concludes that the Prediction of Kidney disease using the Novel Decision Tree (DT) algorithm appears to be significantly better than the Linear Regression(LR) with improved accuracy.

Imprint

Rohith J, Uma Priyadarsini P.S. Comparative Analysis of Chronic Kidney Disease using Novel Decision Tree Algorithm by Comparing Linear Regression for Obtaining Better Accuracy. Cardiometry; Issue 25; December 2022; p.1793-1799; DOI: 10.18137/cardiometry.2022.25.17931799; Available from: https://www.cardiometry.net/issues/no25-december-2022/comparative-analysis-chronic-kidney

Keywords

Chronic Kidney Disease,   Novel Decision Tree,   Linear Regression,   Machine Learning,   Classification,   Diabetes
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