Volume 18, No. 6, 2021

Prediction Of Chronic Kidney Disease Using Data Mining Techniques

Dr. D. Nalini


Kidney failure disease is being observed as a serious challenge to the medical field with its impact on a massive population of the world. Devoid of symptoms, kidney diseases are often identified too late when dialysis is needed urgently. Advanced data mining technologies can help provide alternatives to handle this situation by discovering hidden patterns and relationships in medical data. The objective of this research work is to predict kidney disease by using multiple machine learning algorithms that are Support Vector Machine (SVM), Multilayer Perceptron (MLP), Decision Tree (C4.5), Bayesian Network (BN) and K-Nearest Neighbour (K-NN). The aim of this work is to compare those algorithms and define the most efficient one(s) on the basis of multiple criteria. The database used is “Chronic Kidney Disease” implemented on the WEKA platform. From the experimental results, it is observed that MLP and C4.5 have the best rates. However, when compared with Receiver Operating Characteristic (ROC) curve, C4.5 appears to be the most efficient.

Pages: 7751-7770

Keywords: Chronic Kidney Disease, Data Mining, Feature Selection, Support Vector Machine, Multi-Layered Perceptron, Decision Tree (C4.5), Bayesian Network (BN), K-Nearest Neighbour

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