Predicting Common Diseases Among Students using Decision Tree (J48) Classification Algorithm

Abstract

Predictive analysis is very useful in the process of decision making. It discovers useful information by predicting the future outcome. Nevertheless, it is essential to understand an appropriate technique before the predictive analytical model should be developed. This research will compare two predictive methods which are decision tree technique (using J48 algorithm) and rule induction technique (using JRip algorithm). The aim of this research is to build the predictive model for health datasets of students in one of the universities in Selangor. By analyzing medical profiles such as gender, diseases, the symptoms of the diseases, the organs of the diseases and the body systems of diseases, the model can predict the likelihood of disease that may occur in the future. It can offer significant and important insights, such as the patterns and relationships between medical attributes related to the diagnosis datasets. In this study, we are also able to identify the most common illness that may have infected the students in the past five years. This data analysis could be beneficial specifically to the health center to plan, coordinate tasks and make better decisions.