Predicting the misconception of dengue disease based on the awareness survey

Abrar Noor Akramin Kamarudin, Zurinahni Zainol, Nur Faeza Abu Kassim


Mosquito-borne disease such as dengue fever is a pervasive public health problem around the world and further investigation is needed to rectify the misunderstanding of the disease among communities. This requires a personalized information delivery, which will effectively fix the problem. The process of personalizing information requires several major steps: (i) determine the attributes which will be used to interpret a person, (ii) selects an algorithm which will accurately and efficiently classify the person according to the retrieved background information, and (iii) recommends the correct information to rectify the particular misunderstanding. This research paper considers the first two steps. First, data regarding the knowledge, attitudes and prevention practices are determined from the established literature where some variables give a significant impact on the predictive model. In the second step, five performed machine learning algorithms were tested for the classification task. The result indicates that the use of Support Vector Machine and Decision Tree algorithms provide the best performance in classifying the person’s understanding regarding the dengue fever.


Attitudes; Awareness survey; Dengue; Machine learning; Misconception prediction; Personalization

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