Machine Learning Algorithm Improves Accuracy for analysing Kidney Function Test Using Decision Tree Algorithm

The research has been focused on retrieval of relevant information from the collection of data is a challenging task in data mining techniques [4]. In this research focuses on machine learning algorithm that involves to finding the pattern of health level among the collection of kidney function test to diagnosis and decision making. Data mining is a process or method that extracts interesting knowledge from small or large amounts of dataset, here machine learning algorithm implemented for allow a computer to learn which involves consciousness. An objective of the research is to comparing decision tree algorithm of Random Forest, C4.5 with Bootstrap Aggregation algorithm for analysing the time efficiency and accuracy of the required algorithm. An experiment result is observed the performance of selecting attribute by using two algorithms and analyse the better algorithm in classification.