LSTM-based Forecasting of Dengue Cases in Gujarat: A Machine Learning Approach

Amiben Maheshbhai Mehta; Kajal S Patel

doi:10.17485/IJST/v17i7.2748

Article

LSTM-based Forecasting of Dengue Cases in Gujarat: A Machine Learning Approach

VIEWS 182
PDF 46

Indian Journal of Science and Technology

DOI: 10.17485/IJST/v17i7.2748

Year: 2024, Volume: 17, Issue: 7, Pages: 635-642

Original Article

LSTM-based Forecasting of Dengue Cases in Gujarat: A Machine Learning Approach

Amiben Maheshbhai Mehta¹, Kajal S Patel^2*

¹Research Scholar, Gujarat Technological University, Ahmedabad, Gujarat, India
²Assistant Professor, VGEC, Chandkheda, Gujarat Technological University, Gujarat, India

*Corresponding Author
Email: [email protected]

Received Date:11 February 2023, Accepted Date:23 December 2023, Published Date:14 February 2024

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: Dengue fever, a mosquito-borne viral disease, is particularly prevalent in tropical regions like India. Gujarat State is also one of them. Forecasting outbreaks of diseases such as dengue can prove important for public health management. The purpose of this study is to predict dengue cases in ten districts of Gujarat using the LSTM machine learning model. And if people are aware of this from the beginning, the spread of dengue can be prevented. Methods: This approach uses LSTM models to predict dengue cases using a total of 10 years (2010 to 2019) of data. From this data, data from 2010 to 2016 is used for training and data from 2017 to 2019 is used for testing. To predict dengue cases, population density, average temperature, average humidity, monthly rainfall, dengue cases with lag of one, two and twelve months. Findings: The LSTM model was applied with different parameter configurations, showing the following results: The root mean square error value is 0.04, and the R-squared (R2) score is 0.84. Many machine learning methods, like ANN, linear regression, random forest, etc., have been used to predict dengue cases in different states and countries. LSTM model gives the best results in terms of accuracy. Previously reported dengue cases, population density, and total monthly rainfall proved to be the most effective predictors of dengue in the state of Gujarat. Novelty: Models have been developed to predict dengue outbreaks in many other countries and states. The LSTM model is developed for the first time in this study for the state of Gujarat. 84% accuracy is obtained from the model. This model has been prepared by collecting environmental data and registered dengue cases in Gujarat state.

Keywords: Dengue Cases Predictions, Artificial Intelligence in Healthcare, LSTM Algorithm, Disease Outbreaks, Public Health Management

References

Mussumeci E, Coelho FC. Large-scale multivariate forecasting models for Dengue - LSTM versus random forest regression. Spatial and Spatio-temporal Epidemiology. 2020;35:100372. Available from: https://doi.org/10.1016/j.sste.2020.100372
Santosh T, Ramesh D, Reddy D. LSTM based prediction of malaria abundances using big data. Computers in Biology and Medicine. 2020;124:103859. Available from: https://doi.org/10.1016/j.compbiomed.2020.103859
Ding Y, Zhu Y, Feng J, Zhang P, Cheng Z. Interpretable spatio-temporal attention LSTM model for flood forecasting. Neurocomputing. 2020;403:348–359. Available from: https://doi.org/10.1016/j.neucom.2020.04.110
Chang YS, Chiao HT, Abimannan S, Huang YP, Tsai YT, Lin KM. An LSTM-based aggregated model for air pollution forecasting. Atmospheric Pollution Research. 2020;11(8):1451–1463. Available from: https://doi.org/10.1016/j.apr.2020.05.015
Abdulsalam FI, Yimthiang S, La-Up A, Ditthakit P, Cheewinsiriwat P, Jawjit W. Association between climate variables and dengue incidence in Nakhon Si Thammarat Province, Thailand. Geospatial Health. 2021;16(2):1–14. Available from: https://doi.org/10.4081/gh.2021.1012
Rahman KM, Sharker Y, Rumi RA, Khan MUIUI, Shomik MS, Rahman MW, et al. An Association between Rainy Days with Clinical Dengue Fever in Dhaka, Bangladesh: Findings from a Hospital Based Study. International Journal of Environmental Research and Public Health. 2020;17(24):1–9. Available from: https://doi.org/10.3390/ijerph17249506
Chovatiya M, Dhameliya A, Deokar J, Gonsalves J, Mathur A. Prediction of Dengue using Recurrent Neural Network. In: 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI). (pp. 926-929) IEEE. 2019.
Kakarla SG, Caminade C, Mutheneni SR, Morse AP, Upadhyayula SM, Kadiri MR, et al. Lag effect of climatic variables on dengue burden in India. Epidemiology and Infection. 2019;147:1–10. Available from: https://doi.org/10.1017/s0950268819000608
Nayak SDP, Narayan KA. Prediction of dengue outbreaks in Kerala state using disease surveillance and meteorological data. International Journal Of Community Medicine And Public Health. 2019;6(10):4392–4400. Available from: https://doi.org/10.18203/2394-6040.ijcmph20194500
Jain R, Sontisirikit S, Iamsirithaworn S, Prendinger H. Prediction of dengue outbreaks based on disease surveillance, meteorological and socio-economic data. BMC Infectious Diseases. 2019;19(1):1–16. Available from: https://doi.org/10.1186/s12879-019-3874-x
Doni AR, Sasipraba T. LSTM-RNN Based Approach for Prediction of Dengue Cases in India. Ingénierie des systèmes d information. 2020;25(3):327–335. Available from: https://doi.org/10.18280/isi.250306
Majeed MA, Shafri HZM, Zulkafli Z, Wayayok A. A Deep Learning Approach for Dengue Fever Prediction in Malaysia Using LSTM with Spatial Attention. International Journal of Environmental Research and Public Health. 2023;20(5):1–22. Available from: https://doi.org/10.3390/ijerph20054130
Aleixo R, Kon F, Rocha R, Camargo MS, Camargo RYd. Predicting Dengue Outbreaks with Explainable Machine Learning. In: 2022 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid). Taormina, Italy, 16-19 May 2022. IEEE. p. 940–947.
Mussumeci E, Coelho FC. Machine-learning forecasting for Dengue epidemics - Comparing LSTM, Random Forest and Lasso regression. 2020. Available from: https://doi.org/10.1101/2020.01.23.20018556
Nguyen VH, Tuyet-Hanh TT, Mulhall J, Minh HV, Duong TQ, Chien NV, et al. Deep learning models for forecasting dengue fever based on climate data in Vietnam. PLOS Neglected Tropical Diseases. 2022;16(6):1–22. Available from: https://doi.org/10.1371/journal.pntd.0010509

Copyright

© 2024 Mehta & Patel. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)