Prediction of dengue outbreak in Selangor Malaysia using machine learning techniques

Nurul Azam Mohd Salim, Yap Bee Wah, Caitlynn Reeves, Madison Smith, Wan Fairos Wan Yaacob, Rose Nani Mudin, Rahmat Dapari, Nik Nur Fatin Fatihah Sapri, Ubydul Haque

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Dengue fever is a mosquito-borne disease that affects nearly 3.9 billion people globally. Dengue remains endemic in Malaysia since its outbreak in the 1980’s, with its highest concentration of cases in the state of Selangor. Predictors of dengue fever outbreaks could provide timely information for health officials to implement preventative actions. In this study, five districts in Selangor, Malaysia, that demonstrated the highest incidence of dengue fever from 2013 to 2017 were evaluated for the best machine learning model to predict Dengue outbreaks. Climate variables such as temperature, wind speed, humidity and rainfall were used in each model. Based on results, the SVM (linear kernel) exhibited the best prediction performance (Accuracy = 70%, Sensitivity = 14%, Specificity = 95%, Precision = 56%). However, the sensitivity for SVM (linear) for the testing sample increased up to 63.54% compared to 14.4% for imbalanced data (original data). The week-of-the-year was the most important predictor in the SVM model. This study exemplifies that machine learning has respectable potential for the prediction of dengue outbreaks. Future research should consider boosting, or using, nature inspired algorithms to develop a dengue prediction model.

Original languageEnglish
Article number939
JournalScientific Reports
Volume11
Issue number1
DOIs
StatePublished - Dec 2021

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