Development of Prediction Models for the Dengue Survivability Prediction: An Integration of Data Mining and Decision Support System
Rosalinda B. Guiyab
Rosalinda B. Guiyab , Department of Computing Studies, and Information and Communication Technology, Isabela State University Cabagan, Philippines.
Manuscript received on 02 July 2019 | Revised Manuscript received on 09 July 2019 | Manuscript published on 30 August 2019 | PP: 2199-2205 | Volume-8 Issue-10, August 2019 | Retrieval Number: J94110881019/2019©BEIESP P | DOI: 10.35940/ijitee.J9411.0881019
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Dengue is a viral disease that has been feared by people globally. Due to its rapid prevalence and increasing threat, this study explored on the use of data mining techniques together with decision support system to develop prediction models of dengue survivability. This study was focused on three important points namely: identify significant predictor attributes to dengue survivability prediction, development of a rule-based and decision tree models for dengue survivability prediction, and the development of a dengue survivability platform for prediction purposes. The developed rule-based and decision tree models were compared according to accuracy and they underwent the 10-fold cross validation procedure and were integrated in the system to provide a platform to predict the survivability of a patient given the input medical data using a client-server configuration via the Internet. The result of the prediction for the dengue survivability may be used as an intervention by medical practitioners in the general management of dengue cases.
Keywords: Data Mining, Decision Support System, Dengue, Prediction Models, Survivability.
Scope of the Article: Data Mining