COMPARATIVE STUDY ON CKD USING LEARNING ALGORITHMS
Mounika. B
PG Student, Department of computer engineering, Vignan’s deemed to be university , IN
M. Nirupama Bhat
PG Student, Department of computer engineering, Vignan’s deemed to be university , IN
Keywords: SVM, KNN, Naïve Bayes, Correlation, LASSO,CKD, Performance metrics.
Abstract
Chronic Kidney Disease (CKD) is one of the common disease that conventionally triggered to the diabetic patients. In every country middle aged people were mostly affected due to stress and hypertension. The objective of this project to find the two feature reduction algorithms to differentiate that which one is a better algorithm than one another. The algorithms that are used to reduce the features are Correlation and LASSO (Least Absolute Shrinkage and Selection Operator). After the features reduced some of the learning algorithms like SVM (Support Vector Machine), Naive Bayes, KNN and Logistic Regression were applied, some of the performance metrics also calculated. This experiment is performed on the diabetic patients records. The LASSO feature reduction algorithm gave better accuracy than a correlation algorithm.
How to Cite
References
NA