Machine learning for dengue outbreak prediction: A performance evaluation of different prominent classifiers

Naiyar Iqbal, Mohammad Islam


Dengue disease patients are increasing rapidly and actually dengue has recorded in every continent today according to World Health Organisation (WHO) record. By WHO report the number of dengue outbreak cases announced every year has expanded from 0.4 to 1.3 million during period of 1996 to 2005 and then it has reached to 2.2 to 3.2 million during year of 2010 to 2015 respectively. Consequently, it is fundamental to have a structure that can adequately perceive the pervasiveness of dengue outbreak in a large amount of specimens momentarily. At this critical moment, the capability of seven prominent machine learning systems was assessed for forecast of dengue outbreak. These methods are evaluated by eight miscellaneous performance parameters. LogitBoost ensemble model is reported as the topmost classification accuracy of 92% with sensitivity and specificity of 90 and 94 % respectively.

Full Text:



Althouse, B. M., Ng, Y. Y., & Cummings, D. A. (2011). Prediction of dengue incidence using search query surveillance. PLoS Negl Trop Dis, 5(8), e1258.

Arunachalam, N., Tana, S., Espino, F., Kittayapong, P., Abeyewickrem, W., Wai, K. T., ... & Petzold, M. (2010). Eco-bio-social determinants of dengue vector breeding: a multicountry study in urban and periurban Asia. Bulletin of the World Health Organization, 88(3), 173-184.

Brasier, A. R., Ju, H., Garcia, J., Spratt, H. M., Victor, S. S., Forshey, B. M., ... & Rocha, C. (2012). A three-component biomarker panel for prediction of dengue hemorrhagic fever. The American journal of tropical medicine and hygiene, 86(2), 341-348.

Chadwick, D., Arch, B., Wilder-Smith, A., & Paton, N. (2006). Distinguishing dengue fever from other infections on the basis of simple clinical and laboratory features: application of logistic regression analysis. Journal of Clinical Virology, 35(2), 147-153.

Fathima, A. S., & Manimeglai, D. (2015). Analysis of Significant Factors for Dengue Infection Prognosis Using the Random Forest Classifier. Analysis, 6(2).

Fathima, A., & Manimegalai, D. (2012). Predictive analysis for the arbovirus-dengue using svm classification. International Journal of Engineering and Technology, 2(3), 521-7.

Gibbons, R. V., & Vaughn, D. W. (2002). Dengue: an escalating problem. BMJ: British Medical Journal, 324(7353), 1563.

Horstick, O., Farrar, J., Lum, L., Martinez, E., San Martin, J. L., Ehrenberg, J., ... & Kroeger, A. (2012). Reviewing the development, evidence base, and application of the revised dengue case classification. Pathogens and global health, 106(2), 94-101.

Ibrahim, F., Taib, M. N., Abas, W. A. B. W., Guan, C. C., & Sulaiman, S. (2005). A novel dengue fever (DF) and dengue haemorrhagic fever (DHF) analysis using artificial neural network (ANN). Computer methods and programs in biomedicine, 79(3), 273-281.

Iqbal, N. and Islam, M. (2017) ‘Machine learning for Dengue outbreak prediction: An outlook’, International Journal of Advanced Research in Computer Science, 8(1):93-102.

Kumar, M. N. (2013). Alternating decision trees for early diagnosis of dengue fever. arXiv preprint arXiv:1305.7331.

Nandini, V., Sriranjitha, R., & Yazhini, T. P (2016). Dengue detection and prediction System using data mining with Frequency analysis. Computer Science & Information Technology, [DOI: 10.5121/csit.2016.60906].

Online Available []

Rachata, N., Charoenkwan, P., Yooyativong, T., Chamnongthal, K., Lursinsap, C., & Higuchi, K. (2008, October). Automatic prediction system of dengue haemorrhagic-fever outbreak risk by using entropy and artificial neural network. In Communications and Information Technologies, 2008. ISCIT 2008. International Symposium on (pp. 210-214). IEEE.

Raza, K. (2019). Improving the prediction accuracy of heart disease with ensemble learning and majority voting rule. In U-Healthcare Monitoring Systems (pp. 179-196). Academic Press.

Santamaria, R., Martinez, E., Kratochwill, S., Soria, C., Tan, L. H., Nunez, A., ... & Castelobranco, I. (2009). Comparison and critical appraisal of dengue clinical guidelines and their use in Asia and Latin America. International health, 1(2), 133-140.

Shakil, K. A., Anis, S., & Alam, M. (2015). Dengue disease prediction using weka data mining tool. arXiv preprint arXiv:1502.05167.

Shaukat, K., Masood, N., Mehreen, S., & Azmeen, U. (2015). Dengue Fever Prediction: A Data Mining Problem. Journal of Data Mining in Genomics & Proteomics, 2015.

Souza, L. J. D., Nogueira, R. M. R., Soares, L. C., Soares, C. E. C., Ribas, B. F., Alves, F. P., ... & Pessanha, F. E. B. (2007). The impact of dengue on liver function as evaluated by aminotransferase levels. Brazilian Journal of Infectious Diseases, 11(4), 407-410.

Stany Leena Princy, S., & Muruganandam, A. (2016). An Implementation of Dengue Fever Disease Spread Using Informatica Tool with Special Reference to Dharmapuri District. International Journal of Innovative Research in Computer and Communication Engineering, 4(9). [DOI: 10.15680/IJIRCCE.2016.0409031].

Tanner, L., Schreiber, M., Low, J. G., Ong, A., Tolfvenstam, T., Lai, Y. L., ... & Simmons, C. P. (2008). Decision tree algorithms predict the diagnosis and outcome of dengue fever in the early phase of illness. PLoS Negl Trop Dis,2(3), e196.

Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE transactions on evolutionary computation, 1(1), 67-82.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.