Chronic Renal Failure (CRF) is one of the major disease which affect the human life. The stages of CRF start with loss of renal functions and gradually it leads to complete failure of all kidney functions. This disease is fatal at its end stage unless a replacement of kidney or a dialysis process which is an artificial filtering mechanism is not done. So an early prediction of disease is very important to save the human life. Machine learning is a part of artificial intelligence that uses a variety of techniques to learn from complex dataset. Machine learning techniques are widely used in medical field for disease prediction and prognosis. The objective of this work is to develop a clinical decision support system using machine learning techniques. In this paper first the classification techniques like neural network based back propagation (BPN), probability based Naive Bayes, LDA classifier, lazy learner K Nearest Neighbor (KNN), tree based decision tree, and Random subspace classification algorithms are analyzed. The accuracy of each algorithm found is 81.5%, 78%, 76%, 90%, 93% and 94% respectively on a dataset collected from UCI repository which contains 25 attributes and 400 instances. From the results obtained, the algorithm which gave better result was used for the developing the Clinical Decision Support System.
Dr. Deepa Gopakumar O. S., Ani R., Sasi, G., and Sankar, R., “Decision Support system for diagnosis and prediction of Chronic Renal Failure using Random Subspace Classification”, in 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Jaipur, India, 2016.