Prediction Analysis Of Chronic Kidney Disease Using Novel Decision Tree Algorithm By Comparing Naive Bayes For Obtaining Better Accuracy

Abstract

Aim: Individuals at high-hazard of cardiovascular sickness are no doubt defenseless against ongoing kidney diseases, and historical clinical records can assist with turning away complicated kidney issues. 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 Naive Bayes to find out which of these can give us the best accuracy. Material and Methods: The study used 540 samples with Novel Decision Tree and Naive Bayes 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 Naive Bayes (90.83%) is obtained. There is a statistical significant difference in accuracy for two algorithms is 0.001 (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 Naive Bayes (NB) with improved accuracy.