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Performance Measurements of Machine Learning Approaches for Prediction and Diagnosis of Chronic Kidney Disease (CKD)

Chronic Kidney Disease (CKD) is indicated by gradual degradation of kidney function. Long time complications include heart disease, high blood pressure, bone disease and death of hundreds and thousands of people each year. However, automated early detection of this disease can diminish the mortality rate. More efficient and accurate analysis and models are mandatory to be implemented to negotiate with this situation. To do so, this paper analyzes the key parameters of this disease to be strengthened by incorporating machine learning and data mining approaches together. For early classification of this disease Random Forest, Naïve Bayes, Multilayer Perceptron, Logistic Regression and Neural Network optimized by Adam optimizer have been concentrated on. For further statistical analysis and induction of relationships between attributes, J48 and WEKA tool have been used. Moreover, association rules have been extracted to assist expertise to diagnose this disease. Comparative analysis and experiment depict that Adam-Deep learning outperforms all the approaches by predicting accuracy of 97.34% and J48 demonstrates accurate association rules.