Open AccessPrediction of Diagnosing Chronic Kidney Disease using Machine Learning: Classification Algorithms
Author(s) -
M. Ramakrishna Reddy,
T Devi
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.f3989.049620
Subject(s) - random forest , support vector machine , artificial intelligence , decision tree , classifier (uml) , logistic regression , kidney disease , machine learning , computer science , statistical classification , pattern recognition (psychology) , medicine
Chronic Kidney Disease is a very dangerous health problem that has been spreading as well as growing due to diversification in life style such as food habits, changes in the atmosphere, etc. The branch of biosciences has progressive to a bigger extent and has bring out huge amounts of data from Electronic Health Records. The primary aim of this paper is to classify using various Classification techniques like Logistic Regression (LR), K-Nearest Neighbor (KNN) Classifier, Decision Tree Classifier Tree, Random Forest Classifier, Support Vector Machine (SVM), and SGD Classifier. According to the health statistics of India 63538 cases has been registered on chronic renal disorder. Average age of men and women susceptible to renal disorders occurs within the range of 48 to 70 years.