Schematic Cycle of Case-Based Reasoning Technique Implements in Clinical Decision Support Systems Used for Diagnosis of Liver Disease

Abstract

In this research, schematic cycle of case-based reasoning technique is used for medical data diagnosis & classification. Similarity measurement is the major weakness in executing the CBR technique accurately and efficiently. With the intention of resolving this difficulty we have proposed a similarity algorithm by using Average Weighted Euclidian distance method which calculates distance between the new case and stored cases and then using similarity function to retrieve closely related cases from the case repository. Once the case is retrieved, reinstantiation strategy of case adaption phase is being used for adapting the suggesting way out of the current problem. Then proposed solution of the current problem is revised by the medical expert in the revise phase of the CBR technique. Finally the revised solution is updated or stored into the case repository as a new case. This research performed data analysis on the Indian Liver Patient dataset from UCI Machine Learning Repository is used for diagnosis & classification.