An Illustration of Rheumatoid Arthritis Disease Using Decision Tree Algorithm

Abstract

The Data Mining domain integrates several partitions of the computer science and analytics field. Data mining focuses on mined data from a repository of the dataset to identify patterns, discover knowledge, additionally to predict probable outcomes. Decision tree belongs to classification techniques is a well-known method appropriate for medical diagnosis. Iterative Dichotomiser 3 (ID3) is the general significant algorithm to construct a decision tree. C4.5 is the successor of ID3 that handles dataset contains different numerical attributes. Although many studies have described and compared different decision tree algorithms, some studies have confined paper with analysis and comparison of the decision tree algorithm without the output of the decision tree. One of the inflammatory diseases is Rheumatoid Arthritis (RA) caused by specific autoantibodies with the destruction of synovial joint autoantibodies. Medical dataset applied to construct a decision tree as output has become seldom study. This study elucidates to explore the medical dataset with the decision tree approach and exhibit the derived decision tree output from the RA dataset. The objective of this paper is to construct a decision tree and display the prominent features that predict RA from the RA dataset using the decision tree algorithm.