Expert Medical Systems

Abstract

The Objective of the project is to design an expert system that predicts medical diseases like heart and diabetes diseases with reduced number of attributes. Medical data nowadays is available in abundance but without proper usage. The goal is to turn data that are facts, numbers, or text which can be processed by a computer into information and knowledge. Using data mining techniques on medical data several critical issues can be understood better and dealt with starting from studying risk factors of several diseases to identification of the diseases occurring frequently or taking care of hospital information. The main aim is to analyze the uniqueness of medical data mining using an Expert Medical System. They have potential in clinical laboratory reporting by supporting automation of the process, improving accuracy and consistency, and enhancing the quality of work. Classification of knowledge objects is a knowledge mining and knowledge management process used in grouping similar knowledge objects together. There are plenty of classification algorithms available in literature but decision tree is the most often used because of its ease of implementation and simpler to understand, when compared to other classification algorithms. There are many classifiers but we have used C4.5 for more accuracy and less run time. The decision tree algorithm has been applied on the knowledge of heart and diabetes disease to foretell whether disease is present or not.