Performance Based Evaluation of Various Machine Learning Classification Techniques for Chronic Kidney Disease Diagnosis

TL;DR

This study compares 12 machine learning classification techniques to diagnose Chronic Kidney Disease, finding decision-tree as the most accurate with 98.6% accuracy and high sensitivity and specificity.

Contribution

The paper evaluates and compares multiple machine learning algorithms for CKD diagnosis, highlighting the effectiveness of decision-tree classifiers in medical prediction tasks.

Findings

Decision-tree achieved 98.6% accuracy.

Decision-tree showed high sensitivity and specificity.

Multiple algorithms were systematically compared.

Abstract

Areas where Artificial Intelligence (AI) & related fields are finding their applications are increasing day by day, moving from core areas of computer science they are finding their applications in various other domains.In recent times Machine Learning i.e. a sub-domain of AI has been widely used in order to assist medical experts and doctors in the prediction, diagnosis and prognosis of various diseases and other medical disorders. In this manuscript the authors applied various machine learning algorithms to a problem in the domain of medical diagnosis and analyzed their efficiency in predicting the results. The problem selected for the study is the diagnosis of the Chronic Kidney Disease.The dataset used for the study consists of 400 instances and 24 attributes. The authors evaluated 12 classification techniques by applying them to the Chronic Kidney Disease data. In order to calculate efficiency, results of the prediction by candidate methods were compared with the actual medical results of the subject.The various metrics used for performance evaluation are predictive accuracy, precision, sensitivity and specificity. The results indicate that decision-tree performed best with nearly the accuracy of 98.6%, sensitivity of 0.9720, precision of 1 and specificity of 1.