Dengue Fever Risk Prediction System Using Data Mining Techniques
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Abstract
This research aims to 1) study and measure the effectiveness of a classification model using decision tree-based methods, 2) to develop a dengue fever risk prediction system, and 3) To study the effectiveness of the dengue fever risk prediction system using a sample group created from patient data with dengue fever cases in Nakhon Si Thammarat province over a period of 5 years (B.E. 2558 - B.E. 2563), utilizing data mining techniques for classification using decision trees. The results of the research showed that 1) The model used for predicting dengue fever risk, based on the decision tree classification technique, achieved an accuracy of 83.5%, 2) The dengue fever risk prediction system consists of sub-systems for user authentication, dengue fever incidence data management, dengue fever reporting, management of dengue fever datasets after data mining, and dengue fever risk analysis, and 3) The satisfaction level with the dengue fever risk prediction system was found to be high. ( = 4.06).
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