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Title: Dengue fever prediction modelling using data mining techniques

Authors: Wipawan Buathong; Pita Jarupunphol

Addresses: Department of Digital Technology, Phuket Rajabhat University, Phuket, Thailand ' Department of Digital Technology, Phuket Rajabhat University, Phuket, Thailand

Abstract: This research experiments on several combinations of feature selection and classifier to obtain the most efficient classification model for predicting dengue fever. The features of relationship patterns for predicting dengue fever were investigated. In order to obtain the most effective classification model, several feature selection techniques were ranked and experimented with well-recognised classifiers. The measurement results of different models were illustrated and compared. The most efficient model is the neural network with three layers. Each layer contains 100 nodes with ReLu activation function. Five features were classified using information gain with 64.9% accuracy, 71.8% F-measure, 65.7% precision, and 79.0% recall. Other competitive machine learning models with slightly similar efficiency are: (1) the combined Naive Bayes and information gain; (2) the combined neural network and ReliefF; (3) the combined Naive Bayes and FCBF. SVM, on the other hand, is considered as the least efficient model when experimented with selected feature selection techniques.

Keywords: dengue fever; data mining; classification; feature selection; ranking.

DOI: 10.1504/IJBIDM.2022.10037218

International Journal of Data Mining and Bioinformatics, 2021 Vol.25 No.1/2, pp.103 - 127

Received: 12 May 2020
Accepted: 06 Nov 2020

Published online: 05 Aug 2021 *

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