GET THE APP

..

Journal of Nephrology & Therapeutics

ISSN: 2161-0959

Open Access

Computational Chronic Kidney Disease for Collaborative Healthcare Data Analytics using Random Forest Classification Algorithms

Abstract

V. Shanmugarajeshwari , M. Ilayaraja

Computational Collaborative Healthcare data analytics is a method of methodical data analysis that allows healthcare specialists to discovery opportunities used for development in health system management processing the various information are stored. This proposed approach entails three parts comparable to preprocessing, attribute selection, classification algorithms. The goal of this work is to plan a machine-based diagnostic approach using machine learning technique. This method is developed to mining the risk factors of chronic kidney diseases. In this work, Random forest, SVM C5.0, Decision Tree, C4.5 and ANN algorithms were used to identify an early diagnosis of CKD patients. This work comparing other algorithms the best for Random forest algorithm with good accuracy and less time complexity.

PDF

Share this article

Google scholar citation report
Citations: 647

Journal of Nephrology & Therapeutics received 647 citations as per google scholar report

Journal of Nephrology & Therapeutics peer review process verified at publons

Indexed In

 
arrow_upward arrow_upward