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Data mining has played a significant role in outbreak disease prediction.
However, issues persist due to gaps in existing research based on concerns about low predictive accuracy.
Some algorithms of classification method are weak and the classification methods applied to malaria outbreak prediction are still very rare and more are needed to be established.
To fill these gaps, the overall goal of this book is to propose an ensemble data mining classification method for malaria outbreak prediction that an organization can adopt to obtain better results.
The research used qualitative and quantitative approaches to examine existing results and literature from various sources.
Weka software as a machine learning tool has been used to ensemble DT (LF), LWL, SMO and complement Naive Bayes algorithms.
The results of the study showed that the ensemble data mining classification method for the prediction of malaria outbreak is very accurate and powerful as compared to traditional algorithms.
Hakizimana Leopord has completed his PhD degree in Information Technology and Master’s degree of Science in Computer Science.
He has published various articles in local and international peer-reviewed journals.
His current areas of research interest are Artificial Intelligence, Deep Learning, Data Mining, IoT, and Software Engineering.
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