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Volume 1 , Issue 2 , PP: 12-21, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Marwa M. Eid 1 * , Christos Gatzoulis 2 , Osama Al Abedallat 3
Doi: https://doi.org/10.54216/MOR.010202
This paper explores the potential of machine learning (ML) in revolutionizing the screening and prognosis of dengue fever, a pervasive viral illness transmitted through the bite of infected mosquitoes prevalent in tropical and subtropical areas. Historically, traditional approaches to monitoring outbreaks have been hampered by a lack of precision and timing, creating an opportunity for machine learning to rectify datasets and uncover patterns that enhance accuracy. The paper introduces Random Forests, Support Vector Machines, Neural Networks, and combined classification models, along with their advantages, disadvantages, and the potential for incorporating external data such as climatic factors, population data, real-time Twitter data, etc. The results demonstrate significant increases in accuracy from the models, but it is clear that their applicability is contingent on localized approaches suitable for the regions. This underscores the importance of the quality and completeness of data used in the models. Current research indicates that data availability and the refinement of these models require a collective approach. The work underscores the potential of ML to redefine the paradigm of outbreak management in dengue and other vector-borne diseases, offering hope for improved public health worldwide.
Dengue prediction , Machine learning , Outbreak detection , Climatic data , Neural networks , Public health
[1] M. Cabrera, J. Leake, J. Naranjo-Torres, N. Valero, J. C. Cabrera, and A. J. Rodríguez-Morales, “Dengue Prediction in Latin America Using Machine Learning and the One Health Perspective: A Literature Review,” Trop Med Infect Dis, vol. 7, no. 10, p. 322, Oct. 2022, doi: 10.3390/TROPICALMED7100322.
[2] A. Abdulrahman, .. Z. M., A. M. Zaki, F. H. H.Rizk, M. M. Eid, and E.-S. M. EL EL-Kenawy, “Exploring Optimization Algorithms: A Review of Methods and Applications,” Journal of Artificial Intelligence and Metaheuristics, vol. 7, no. 2, pp. 08–17, 2024, doi: 10.54216/JAIM.070201.
[3] V. H. Nguyen et al., “Deep learning models for forecasting dengue fever based on climate data in Vietnam,” PLoS Negl Trop Dis, vol. 16, no. 6, p. e0010509, Jun. 2022, doi: 10.1371/JOURNAL.PNTD.0010509.
[4] F. I. Abdulsalam, P. Antunez, S. Yimthiang, and W. Jawjit, “Influence of climate variables on dengue fever occurrence in the southern region of Thailand,” PLOS Global Public Health, vol. 2, no. 4, p. e0000188, Apr. 2022, doi: 10.1371/JOURNAL.PGPH.0000188.
[5] S. Patil and S. Pandya, “Forecasting Dengue Hotspots Associated With Variation in Meteorological Parameters Using Regression and Time Series Models,” Front Public Health, vol. 9, p. 798034, Nov. 2021, doi: 10.3389/FPUBH.2021.798034/BIBTEX.
[6] A. L. Buczak, B. Baugher, L. J. Moniz, T. Bagley, S. M. Babin, and E. Guven, “Ensemble method for dengue prediction,” PLoS One, vol. 13, no. 1, p. e0189988, Jan. 2018, doi: 10.1371/JOURNAL.PONE.0189988.
[7] M. A. Majeed, H. Z. M. Shafri, Z. Zulkafli, and A. Wayayok, “A Deep Learning Approach for Dengue Fever Prediction in Malaysia Using LSTM with Spatial Attention,” Int J Environ Res Public Health, vol. 20, no. 5, p. 4130, Mar. 2023, doi: 10.3390/IJERPH20054130.
[8] S. Anno et al., “Challenges and implications of predicting the spatiotemporal distribution of dengue fever outbreak in Chinese Taiwan using remote sensing data and deep learning,” Geo-spatial Information Science, vol. 27, no. 4, pp. 1155–1161, Jul. 2024, doi: 10.1080/10095020.2022.2144770.
[9] S. T. da Silva et al., “When climate variables improve the dengue forecasting: a machine learning approach,” The European Physical Journal Special Topics 2024, pp. 1–15, Jun. 2024, doi: 10.1140/EPJS/S11734-024-01201-7.
[10] A. Sebastianelli et al., “A reproducible ensemble machine learning approach to forecast dengue outbreaks,” Scientific Reports 2024 14:1, vol. 14, no. 1, pp. 1–17, Feb. 2024, doi: 10.1038/s41598-024-52796-9.
[11] J. C. Addawe, J. D. L. Caro, and R. A. B. Juayong, “Machine Learning Methods for Modeling Dengue Incidence in Local Communities,” Lecture Notes in Networks and Systems, vol. 556 LNNS, pp. 392–400, 2023, doi: 10.1007/978-3-031-17601-2_38/FIGURES/3.
[12] C. Souza, P. Maia, L. M. Stolerman, V. Rolla, and L. Velho, “Predicting dengue outbreaks in Brazil with manifold learning on climate data,” Expert Syst Appl, vol. 192, p. 116324, Apr. 2022, doi: 10.1016/J.ESWA.2021.116324.
[13] K. Laoprasopwattana, L. Kaewjungwad, R. Jarumanokul, and A. Geater, “Differential diagnosis of Chikungunya, dengue viral infection and other acute febrile illnesses in children,” Pediatr Infect Dis J, vol. 31, no. 5, pp. 459–463, May 2012, doi: 10.1097/INF.0B013E31824BB06D.
[14] T. J. Schaefer, P. K. Panda, and R. W. Wolford, “Dengue Fever,” BMJ Best Practice, pp. 5–6, Mar. 2024, Accessed Oct. 22, 2024. [Online]. Available: https://www.ncbi.nlm.nih.gov/books/NBK430732/
[15] H. J. M. Hendri and H. Sulaiman, “Predictive modeling for dengue patient’s length of stay (LoS) using big data analytics (BDA),” Lecture Notes on Data Engineering and Communications Technologies, vol. 5, pp. 12–19, 2018, doi: 10.1007/978-3-319-59427-9_2.
[16] S. A. Kularatne and C. Dalugama, “Dengue infection: Global importance, immunopathology and management,” Clinical Medicine, vol. 22, no. 1, p. 9, Jan. 2022, doi: 10.7861/CLINMED.2021-0791.
[17] N. Sumanasinghe, A. R. Mikler, J. Muthukudage, C. Tiwari, and R. Quiroz, “Data driven prediction of dengue incidence in Thailand,” Advances in Intelligent Systems and Computing, vol. 566, pp. 95–107, 2018, doi: 10.1007/978-3-319-60663-7_9/FIGURES/3.
[18] N. M. Rajendran, M. Karthikeyan, B. Karthik Raja, K. Pragadishwaran, E. A. Gopalakrishnan, and V. Sowmya, “Communicable Disease Prediction Using Machine Learning and Deep Learning Algorithms,” Lecture Notes in Networks and Systems, vol. 757 LNNS, pp. 979–992, 2023, doi: 10.1007/978-981-99-5166-6_66/FIGURES/13.
[19] P. Guo et al., “An ensemble forecast model of dengue in Guangzhou, China using climate and social media surveillance data,” Science of The Total Environment, vol. 647, pp. 752–762, Jan. 2019, doi: 10.1016/J.SCITOTENV.2018.08.044.
[20] J. Daniel et al., “Recurrent Neural Networks for Feature Extraction from Dengue Fever,” 2022, doi: 10.1155/2022/5669580.
[21] S. Raizada, S. Mala, and A. Shankar, “Vector-Borne Disease Outbreak Prediction Using Machine Learning Techniques,” EAI/Springer Innovations in Communication and Computing, pp. 227–241, 2021, doi: 10.1007/978-3-030-66519-7_9/FIGURES/2.
[22] F. Ibrahim, M. N. Taib, W. A. B. W. Abas, C. C. Guan, and S. Sulaiman, “A novel dengue fever (DF) and dengue haemorrhagic fever (DHF) analysis using artificial neural network (ANN),” Comput Methods Programs Biomed, vol. 79, no. 3, pp. 273–281, Sep. 2005, doi: 10.1016/J.CMPB.2005.04.002.
[23] Z. Mustaffa et al., “Predictive Analysis of Dengue Outbreak Based on an Improved Salp Swarm Algorithm," doi: 10.2478/cait-2020-0053.
