Data Mining Techniques for Predicting Immunize-able Diseases: Nigeria as a Case Study

Abstract

Disease rates vary between different locations particularly in the rural areas. While a database of diseases occurrence could be easily found, studies have been limited to descriptive statistical analysis, and are mostly restricted to diseases affecting adults. This paper therefore presents a Mathematical Model (MM) for predicting immunize-able diseases that affect children between ages 0 -5 years. The model was adapted and deployed for use in six (6) selected localized areas within Osun State in Nigeria. Using the MATLAB's ANN toolbox, the Statistics toolbox for classification and regression, and the Naïve Bayesian classifier the MM was developed. The MM is robust in that it takes advantage of three (3) data mining techniques: ANN, Decision Tree Algorithm and Naïve Bayes Classifier. These data mining techniques provided the means by which hidden information were discovered for detecting trends within databases, and thus facilitate the prediction of future disease occurrence in the tested locations. Results obtained showed that diseases have peak periods depending on their epidemicity, hence the need to adequately administer immunization to the right places at the right time. Therefore, this paper argues that using this model would enhance the effectiveness of routine immunization in Nigeria.

Child Immunization Coverage – A Critical Review

“…al. [17] presented a mathematical model for predicting immunizable diseases in Nigeria that affect children between age 0-5 years. They have applied three data mining techniques namely ANN, Decision tree and Naïve Bayes Classifier to uncover hidden information.…”