Data Mining Techniques for Predicting Immunize-able Diseases: Nigeria as a Case Study
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Date
2013-05
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Publisher
Foundation of Computer Science FCS, New York, USA
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.
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Keywords
Data mining techniques, Immunize-able diseases, Decision tree algorithm, Databases, Predictive Model, Matlab
Citation
Idowu PA, Akhigbe BI, Adeosun, OO, Kayode AA and Osungbade AI (2013). Data Mining Techniques for Predicting Immunize-able Diseases: Nigeria as a Case Study. International Journal of Applied Information Systems (IJAIS), Vol.5, No. 7, pp 5-15.