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Original research

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

Abstract

Aim: Currently kidney disease is a major problem. Because there are so many people with this disease. Kidney disease is very dangerous if not immediately treated on time, and may be fatal. The main objective of this study aims to find the best-suited algorithm that will give us the most ideal prediction. The Novel Decision Tree is compared to Logistic regression to find out which of these can give us the best accuracy. Material and Methods: The study used 220 samples with Novel Decision Tree and Logistic 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 Logistic 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 Logistic regression (LR) with improved accuracy.

Imprint

Rohith J, Uma Priyadarsini P.S. An Analysis Of Chronic Kidney Disease Using Novel Decision Tree Algorithm By Comparing Logistic Regression For Obtaining Better Accuracy. Cardiometry; Issue 25; December 2022; p.1779-1785; DOI: 10.18137/cardiometry.2022.25.17791785; Available from: https://www.cardiometry.net/issues/no25-december-2022/analysis-chronic-kidney-disease

Keywords

Chronic Kidney Disease,   Novel Decision Tree algorithm,   Logistic Regression,   Machine learning,   Classification,   Diabetes
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