Open AccessImproving Classifier Accuracy for diagnosing Chronic Kidney Disease Using Support Vector Machines
Author(s) -
Chhotu Kumar,
AUTHOR_ID,
P. Thangaraju,
AUTHOR_ID
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.f9377.088619
Subject(s) - support vector machine , computer science , artificial intelligence , normalization (sociology) , machine learning , kidney disease , classifier (uml) , perceptron , multilayer perceptron , pattern recognition (psychology) , data mining , artificial neural network , medicine , sociology , anthropology
Preventing Chronic Kidney Disease has become one of the most intriguing task to the healthcare society. The major objective of this paper is to deal mainly with different classification algorithms namely NaiveBayes, Multi Layer Perceptron and Support Vector Machine. The work analyzes the Chronic Kidney Disease dataset taken from the machine learning repository of UCI. Pre-processing techniques such as missing value replacement, unsupervised discretization and normalization are applied to the Chronic Kidney Disease dataset to improve accuracy. Accuracy and time are the taken as the experimental outcomes of the classification models. The final conclusion states that Support Vector Machine implements much superior than all the other classification methods.