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Dengue outbreaks prediction in Bangladesh perspective using distinct multilayer perceptron NN and decision tree

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Abstract

Dengue fever is a disease that has been outbreak worldwide in the last few years. Dengue is a fatal disease; sometimes, it may cause life-threatening complications and even death. Dengue is considered to be one of the critical diseases which is spreading in more than 110 countries. Nearly 45,000 case reports have been found around Bangladesh in the last year. Dengue fever has become a major health hazard in Bangladesh. Hence, early detection would mitigate major casualties of Dengue disease. Distinct studies have been performed concerning Dengue disease; however, no effective study, particularly from Bangladesh's perspective, it seemed that reveals Dengue outbreaks prediction method. In this scenario, this research work aims to analyse the Dengue disease and build an apposite model to predict dengue outbreaks. This paper also aims to find the best technique to early predicts Dengue disease. The real-time data of the patients admitted to different hospitals in Bangladesh is accumulated to achieve the goal of the current research. Then different multilayer perceptron neural networks and a Decision tree are used for Dengue outbreaks prediction. Twenty-five parameters are analysed to find these parameters' infection rates in this work. A comparative study of the developed models' performances is also accomplished to obtain a better Dengue outbreaks prediction model. The results evidence that the Levenberg–Marquardt is the best technique with 97.3% accuracy and 2.7% error in Dengue disease prediction. On the other hand, the Decision tree may have the second choice to assess Dengue disease.

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Acknowledgements

We are thankful to the distinct hospitals suited at Noakhali and Dhaka, including General Hospital, Sadar Noakhali; Prime Hospital, Sadar, Noakhali; Good Heal Hospital, Sadar, Noakhali. We also want to express our earnest gratitude to the patients who provided the required data. Besides, we acknowledge the persons working in the Laboratory of Information and Communication Engineering Department, Noakhali Science and Technology University, who have always helped us complete this work successfully. Finally, but not least, we are very much thankful to the Research Cell, Noakhali Science and Technology University, for her small financial support.

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Authors and Affiliations

  1. Department of Information and Communication Engineering, Noakhali Science and Technology University, Noakhali, 3814, Bangladesh

    Md. Ashikur Rahman Khan, Jony Akter, Sabbir Ejaz & Tanvir Jaman Khan

  2. Department of Computer Science and Engineering, Prime University, Dhaka, Bangladesh

    Ishtiaq Ahammad

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Correspondence to Md. Ashikur Rahman Khan.

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Conflict of interest

Md. Ashikur Rahman Khan has given the idea for this research work. Moreover, he has contributed to carrying out the work, model development, and accomplishing paper revision. Jony Akter has contributed to data collection, model training and testing, and writing paper. Ishtiaq Ahammad has played a role in writing the paper, preparing it for the journal article, and paper submission. Sabbir Ejaz and Tanvir Zaman Khan have played a significant role in the paper's revision and submission.

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It is declared that this work is original, entirely authentic, and the data used data are genuine. The work has been performed using very recent data. Neither the data nor the text/content from a similar paper has been copied. It is firmly stated that the paper is entirely original, and all the authors have significant roles and contributions to completing this work and preparing the paper.

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Khan, M.A.R., Akter, J., Ahammad, I. et al. Dengue outbreaks prediction in Bangladesh perspective using distinct multilayer perceptron NN and decision tree. Health Inf Sci Syst 10, 32 (2022). https://doi.org/10.1007/s13755-022-00202-x

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