Abstract
Malaria has been identified to be one of the most common diseases with a great public health problem globally and it is caused by mosquitos’ parasites. This prevails in developing nations where healthcare facilities are not enough for the patients. The technological advancement in medicine has resulted in the collection of huge volumes of data from various sources in different formats. A reliable and early parasite-based diagnosis, identification of symptoms, disease monitoring, and prescription are crucial to decreasing malaria occurrence in Nigeria. Hence, the use of deep and machine learning models is essentials to reduce the effect of malaria-endemic and for better predictive models. Therefore, this paper proposes a framework to predict malaria-endemic in Nigeria. To predict the malaria-endemic well, both environmental and clinical data were used using Kwara State as a case study. The study used a deep learning algorithm as a classifier for the proposed system. Three locations were selected from Irepodun Local Government Areas of Kwara State with 34 months periodic pattern. Each location reacted differently based on environmental factors. The findings indicate that both factors are significant in malaria prediction and transmission. The LSTM algorithm provides an efficient method for detecting situations of widespread malaria.
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References
Kiang, R., et al.: Meteorological, environmental remote sensing, and neural network analysis of the epidemiology of malaria transmission in Thailand. Geospatial Health, 71–84 (2006)
Wilke, A.B., Beier, J.C., Benelli, G.: The complexity of the relationship between global warming and urbanization–an obscure future for predicting increases in vector-borne infectious diseases. Current Opinion Insect Sci. 35, 1–9 (2019)
Benelli, G.: Green synthesized nanoparticles in the fight against mosquito-borne diseases and cancer—a brief review. Enzyme Microb. Technol. 95, 58–68 (2016)
Wilder-Smith, A., Gubler, D.J., Weaver, S.C., Monath, T.P., Heymann, D.L., Scott, T.W.: Epidemic arboviral diseases: priorities for research and public health. Lancet. Infect. Dis 17(3), e101–e106 (2017)
Rabinovich, R.N., et al.: malERA: an updated research agenda for malaria elimination and eradication. PLoS Med. 14(11), e1002456 (2017)
Zolnikov, T.R.: Vector-borne disease. In: Autoethnographies on the Environment and Human Health, pp. 113–126. Palgrave Macmillan, Cham (2018)
Sougoufara, S., Ottih, E.C., Tripet, F.: The need for new vector control approaches targeting outdoor biting Anopheline malaria vector communities. Parasit. Vectors 13(1), 1–15 (2020)
Christophers, S.R.: Epidemic malaria of the Punjab: with a note of a method of predicting epidemic years. Trans. Committee Stud. Malaria India 2, 17–26 (1911)
Awotunde, J.B., Matiluko, O.E., Fatai, O.W.: Medical diagnosis system using fuzzy logic. Afr. J. Comp. ICT 7(2), 99–106 (2014)
Ayo, F.E., Awotunde, J.B., Ogundokun, R.O., Folorunso, S.O., Adekunle, A.O.: A decision support system for multi-target disease diagnosis: a bioinformatics approach. Heliyon 6(3), e03657 (2020)
Zinszer, K., et al.: Forecasting malaria in a highly endemic country using environmental and clinical predictors. Malaria J. 14(1), 245 (2015)
Rochlin, I., Ninivaggi, D.V., Benach, J.L.: Malaria and Lyme disease-the the largest vector-borne US epidemics in the last 100 years: success and failure of public health. BMC Public Health 19(1), 804 (2019)
WHO: World malaria report 2013. World Health Organization, Geneva (2013)
Adebiyi, M., et al.: Computational investigation of consistency and performance of the biochemical network of the malaria parasite, Plasmodium falciparum. In: Misra, S., et al. (eds.) ICCSA 2019. LNCS, vol. 11623, pp. 231–241. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-24308-1_19
Mutabingwa, T.K.: Artemisinin-based combination therapies (ACTs): best hope for malaria treatment but inaccessible to the needy! Acta Trop. 95, 305–315 (2005)
Leslie, T., et al.: Overdiagnosis and mistreatment of malaria among febrile patients at primary healthcare level in Afghanistan: an observational study. BMJ 345, e4389 (2012)
Bastiaens, G.J.H., Bousema, T., Leslie, T.: Scale-up of malaria rapid diagnostic tests and artemisinin-based combination therapy: challenges and perspectives in sub-Saharan Africa. PLoS Med. 11, e1001590 (2014)
Abisoye, O.A., Jimoh, R.G.: A hybrid intelligent forecasting model to determine malaria transmission. AIT 2015, 5 (2015)
Oladele, T.O., Ogundokun, R.O., Awotunde, J.B., Adebiyi, M.O., Adeniyi, J.K.: Diagmal: a malaria coactive neuro-fuzzy expert system. In: Gervasi, O., et al. (eds.) ICCSA 2020. LNCS, vol. 12254, pp. 428–441. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58817-5_32
Ayo, F.E., Ogundokun, R.O., Awotunde, J.B., Adebiyi, M.O., Adeniyi, A.E.: Severe acne skin disease: a fuzzy-based method for diagnosis. In: Gervasi, O., et al. (eds.) ICCSA 2020. LNCS, vol. 12254, pp. 320–334. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58817-5_25
Davis, J.K., et al.: A genetic algorithm for identifying spatially-varying environmental drivers in a malaria time series model. Environ. Model Softw. 119, 275–284 (2019)
Gomez-Elipe, A., Otero, A., Van Herp, M., Aguirre-Jaime, A.: Forecasting malaria incidence based on monthly case reports and environmental factors in Karuzi, Burundi, 1997–2003. Malaria J. 6(1), 129 (2007)
Santosh, T., Ramesh, D., Reddy, D.: LSTM based prediction of malaria abundances using big data. Comput. Biol. Med. 124, 103859 (2020)
Wang, Y., Kung, L., Byrd, T.A.: Big data analytics: understanding its capabilities and potential benefits for healthcare organizations. Technol. Forecast. Soc. Chang. 126, 3–13 (2018)
Choi, T.M., Chan, H.K., Yue, X.: Recent development in big data analytics for business operations and risk management. IEEE Trans. Cybern. 47(1), 81–92 (2016)
Donoho, D.: 50 years of data science. J. Comput. Graph. Stat. 26(4), 745–766 (2017)
Shi, B., Iyengar, S.S.: General framework of mathematics. Mathematical Theories of Machine Learning - Theory and Applications, pp. 13–16. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-17076-9_2
Okewu, E., Misra, S., Lius, F.-S.: Parameter tuning using adaptive moment estimation in deep learning neural networks. In: Gervasi, O., et al. (eds.) ICCSA 2020. LNCS, vol. 12254, pp. 261–272. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58817-5_20
Blei, D.M., Smyth, P.: Science and data science. Proc. Natl. Acad. Sci. 114(33), 8689–8692 (2017)
Hulsen, T., et al.: From big data to precision medicine. Front. Med. 6, 34 (2019)
Baro, E., Degoul, S., Beuscart, R., Chazard, E.: Toward a literature-driven definition of big data in healthcare. BioMed Res. Int. 2015 (2015)
Saweros, E., Song, Y.-T.: Connecting heterogeneous electronic health record systems using tangle. In: Lee, S., Ismail, R., Choo, H. (eds.) IMCOM 2019. AISC, vol. 935, pp. 858–869. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-19063-7_68
Austin, C., Kusumoto, F.: The application of Big Data in medicine: current implications and future directions. J. Interv. Card. Electrophysiol. 47(1), 51–59 (2016)
Fiske, A., Buyx, A., Prainsack, B.: Health information counselors: a new profession for the age of big data. Acad. Med. 94(1), 37 (2019)
Galetsi, P., Katsaliaki, K., Kumar, S.: Values, challenges, and future directions of big data analytics in healthcare: a systematic review. Soc. Sci. Med., 112533 (2019)
Williamson, B.: Big Data in Education: The Digital Future of Learning, Policy, and Practice. Sage, London (2017)
Krumholz, H.M.: Big data and new knowledge in medicine: the thinking, training, and tools needed for a learning health system. Health Aff. 33(7), 1163–1170 (2014)
Lacroix, P.: Big data privacy and ethical challenges. In: Househ, M., Kushniruk, Andre W., Borycki, Elizabeth M. (eds.) Big Data, Big Challenges: A Healthcare Perspective. LNB, pp. 101–111. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-06109-8_9
Metaxiotis, K.: Healthcare knowledge management. In: Encyclopedia of Knowledge Management, 2nd edn., pp. 366–375. IGI Global (2011)
Halder, P., Pan, I.: Role of Big data analysis in healthcare sector: a survey. In: 2018 Fourth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), pp. 221–225. IEEE, November 2018
Dai, H.N., Wang, H., Xu, G., Wan, J., Imran, M.: Big data analytics for manufacturing internet of things: opportunities, challenges, and enabling technologies. Enterp. Inf. Syst., 1–25 (2019)
Olaronke, I., Oluwaseun, O.: Big data in healthcare: prospects, challenges, and resolutions. In: 2016 Future Technologies Conference (FTC), pp. 1152–1157. IEEE, December 2016
Tresp, V., Overhage, J.M., Bundschus, M., Rabizadeh, S., Fasching, P.A., Yu, S.: Going digital: a survey on digitalization and large-scale data analytics in healthcare. Proc. IEEE 104(11), 2180–2206 (2016)
Oussous, A., Benjelloun, F.Z., Lahcen, A.A., Belfkih, S.: Big data technologies: a survey. J. King Saud Univ.-Comput. Inf. Sci. 30(4), 431–448 (2018)
Sagiroglu, S., Sinanc, D.: Big data: a review. In: 2013 International Conference on Collaboration Technologies and Systems (CTS), pp. 42–47. IEEE (2013)
Villars, R.L., Olofson, C.W., Eastwood, M.: Big data: what it is and why you should care. White Paper, IDC, 14, 1–14 (2011)
Priyanka, K., Kulennavar, N.: A survey on big data analytics in health care. Int. J. Comput. Sci. Inf. Technol. 5(4), 5865–5868 (2014)
Kruse, C.S., Goswamy, R., Raval, Y.J., Marawi, S.: Challenges and opportunities of big data in health care: a systematic review. JMIR Medical Inform. 4(4), e38 (2016)
Abayomi-Alli, A., Abayomi-Alli, O., Vipperman, J., Odusami, M., Misra, S.: Multi-class classification of impulse and non-impulse sounds using deep convolutional neural network (DCNN). In: Misra, S., et al. (eds.) ICCSA 2019. LNCS, vol. 11623, pp. 359–371. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-24308-1_30
Birkhead, G.S., Klompas, M., Shah, N.R.: Use of electronic health records for public health surveillance to advance public health. Annu. Rev. Public Health 36, 345–359 (2015)
Cohen, I.G., Amarasingham, R., Shah, A., Xie, B., Lo, B.: The legal and ethical concerns that arise from using complex predictive analytics in health care. Health Aff. 33(7), 1139–1147 (2014)
Ram, S., Zhang, W., Williams, M., Pengetnze, Y.: Predicting asthma-related emergency department visits using big data. IEEE J. Biomed. Health Inform. 19(4), 1216–1223 (2015)
Santosh, T., Ramesh, D.: DENCLUE-DE: differential evolution based DENCLUE for scalable clustering in big data analysis. In: Smys, S., Senjyu, T., Lafata, P. (eds.) ICCNCT 2019. LNDECT, vol. 44, pp. 436–445. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-37051-0_50
Ayeni, F., Misra, S., Omoregbe, N.: Using big data technology to contain current and future occurrence of ebola viral disease and other epidemic diseases in West Africa. In: Tan, Y., Shi, Y., Buarque, F., Gelbukh, A., Das, S., Engelbrecht, A. (eds.) ICSI 2015. LNCS, vol. 9142, pp. 107–114. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20469-7_13
Behera, R.K., Jena, M., Rath, S.K., Misra, S.: Co-LSTM: convolutional LSTM model for sentiment analysis in social big data. Inf. Process. Manage. 58(1), 102435 (2021)
Ward, P.R.: Improving access to, use of, and outcomes from public health programs: the importance of building and maintaining trust with patients/clients. Front. Public Health 5, 22 (2017)
Okewu, E., Misra, S., Fernandez, S.L., Ayeni, F., Mbarika, V., Damaševičius, R.: Deep neural networks for curbing climate change-induced farmers-herdsmen clashes in a sustainable social inclusion initiative. Problemy Ekorozwoju 14(2) (2019)
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Awotunde, J.B., Jimoh, R.G., Oladipo, I.D., Abdulraheem, M. (2021). Prediction of Malaria Fever Using Long-Short-Term Memory and Big Data. In: Misra, S., Muhammad-Bello, B. (eds) Information and Communication Technology and Applications. ICTA 2020. Communications in Computer and Information Science, vol 1350. Springer, Cham. https://doi.org/10.1007/978-3-030-69143-1_4
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