Decision Tree Models for Predicting the Effect of Electronic Waste on Human Health

Abstract

Informal processing of electronic waste has become one of the commonest sources of employment in developing countries which has contracted a great impact on human health due to the improper disposal of the heavy metals found in these waste materials. Several research works have been conducted to predict e-waste generation and management. Unfortunately, there is no study to predict the disease associated with the activities of informal e-waste products and their disposal. This study predicts the categorized disease of a person working and/or living at an electronic waste dump site based on their activities and their lifestyle using decision tree algorithms. The categorized diseases are skin, respiratory and reproductive diseases. The work compared the performance of C4.5 algorithm which used the Chi-squared test for tree pruning to handle overfitting with the Classification and Regression Tree (CART) algorithm which used tree depth control to handle overfitting. The C4.5 algorithm proved to be more effective than the CART algorithm. The study recommends that whenever two or more algorithms can be used to handle the same problem in principle, they should all be used and their results be compared.