The Predicting Diseases of Employees with VASA Dataset using Entropy

Abstract

Predicting diseases of employees under work pressure is one of the most common issue affecting most of the people around the world. In this paper, a disease prediction model is constructed and it helps the employees for predicting the disease based on real data which is collected from different employees and staffs in various organizations. In the real dataset has six important attributes such as age, sex, nature of works, working sector, occupation, work pressure. The values for fifty various employees is contained in the sample dataset. As per the sample data is created a decision tree using decision tree induction method.  Based the selection of nodes in the tree, using Entropy or Information Gain computation for each attribute. Splitting attribute is the right side attribute if it provides the very best Information Gain at each level of the tree.

Keywords: Disease Prediction, decision tree induction method, classification, entropy, information gain.