Abstract
Malaria resurgence significantly threatens progress made towards malaria elimination in the past years and consequently increases socioeconomic and public health burden, especially in developing countries. This is exacerbated by the lack of intelligent models for predicting, mapping, diagnosing, and detecting malaria to strengthen malaria prevention and control measures. Predicting malaria and understanding risk factors leading to malaria outbreaks can assist policymakers in re-strategizing and re-aligning malaria elimination strategies and optimizing resource allocation by prioritizing malaria-endemic areas. Therefore, this study provides a comprehensive review of machine learning techniques applied to predict malaria using various risk factors. The study revealed that despite the distribution of mosquito nets, indoor spraying of insecticides, community engagement programmes and awareness strategies, socioeconomic factors, climate and environmental conditions significantly contribute towards malaria outbreaks and remain underexploited and poorly understood. Socioeconomic factors such as lower income, living conditions with house type, distance to health facilities, availability, and use of mosquito nets influence malaria outbreaks. Climatic and environmental risk factors including land surface temperature, rainfall, humidity, enhanced vegetation index, normalized difference vegetation index, and normalized difference water index significantly influence malaria incidences. The study further revealed that machine learning models such as support vector machines, decision trees, random forests, Extreme Gradient Boosting, logistic regression, K-Nearest Neighbors, Naïve Bayes, and multilayer perceptron have been greatly used to predict malaria using socioeconomic, climatic and environmental data. Predicting malaria can assist to develop early malaria warning systems, redesign interventions, make informed decision-making and subsequently strengthening malaria prevention and control measures.
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References
Tian, H., et al.: Malaria elimination on Hainan Island despite climate change. Commun. Med. 21(2), 1–9 (2022 ) https://doi.org/10.1038/s43856-022-00073-z
Mbunge, E., Ndumiso, N., Kavu, T.D., Dandajena, K., Batani, J., Fashoto, S.G:. Towards QR Code Health Systems Amid COVID-19: Lessons Learnt from Other QR Code Digital Technologies, pp. 129–43 (2022). https://doi.org/10.1007/978-3-031-10031-4_7
Kebede, Y., et al.: Primary school students’ poetic malaria messages from Jimma zone, Oromia, Ethiopia: a qualitative content analysis. BMC Public Health 21, 1–16 (2021). https://doi.org/10.1186/S12889-021-11641-8/TABLES/2
Mbunge, E., Millham, R., Sibiya, M.N., Takavarasha, S.: Impact of COVID-19 on Malaria Elimination: Juxtaposing Indoor Residual Spraying and Mobile Phones in Buhera Rural District, Zimbabwe (2021). https://doi.org/10.21203/rs.3.rs-173130/v2
Tarekegn, M., Tekie, H., Dugassa, S., Wolde-Hawariat, Y.: Malaria prevalence and associated risk factors in Dembiya district North-western Ethiopia. Malar J. 20, 1–11 (2021). https://doi.org/10.1186/S12936-021-03906-9/TABLES/4
Mohammed, M.A., Hong, T.: Role of vector control in fighting against malaria: Evidence from Ethiopian health-related indicators. J. Infect. Public Health 14, 527–532 (2021). https://doi.org/10.1016/J.JIPH.2020.12.002
Cheng, B., Htoo, S.N., Mhote, N.P.P., Davison, C.M.: A systematic review of factors influencing participation in two types of malaria prevention intervention in Southeast Asia. Malar J. 20, 1–9 (2021). https://doi.org/10.1186/S12936-021-03733-Y/FIGURES/1
Mbunge, E., Millham, R., Sibiya, N., Takavarasha, S.: Is malaria elimination a distant dream? Reconsidering malaria elimination strategies in Zimbabwe. Public Heal Pract. 2, 100168 (2021)
Li, X.H., et al.: Seven decades towards malaria elimination in Yunnan, China. Malar J. 20, 1–16 (2021). https://doi.org/10.1186/S12936-021-03672-8/TABLES/2
Kamndaya, M., Mfipa, D., Lungu, K.: Household knowledge, perceptions and practices of mosquito larval source management for malaria prevention and control in Mwanza district, Malawi: a cross-sectional study. Malar J. 20, 1–8 (2021). https://doi.org/10.1186/S12936-021-03683-5/TABLES/3
Dong, S., Dong, Y., Simões, M.L., Dimopoulos, G.: Mosquito transgenesis for malaria control. Trends Parasitol 38, 54–66 (2022). https://doi.org/10.1016/J.PT.2021.08.001
Mbunge, E., Millham, R.C., Sibiya, M.N., Takavarasha, S.: Diverging mobile technology’s cognitive techniques into tackling malaria in sub-saharan africa: a review. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds.) CoMeSySo 2021. LNNS, vol. 232, pp. 679–699. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-90318-3_54
Mbunge, E., Sibiya, M.N., Millham, R.C., Takavarasha, S.: Micro-spatial modelling of malaria cases and environmental risk factors in Buhera rural district, Zimbabwe. In: 2021 Conference on Information Communications Technology and Society ICTAS 2021 - Proceedings, pp. 2–8 (2021). https://doi.org/10.1109/ICTAS50802.2021.9394987
Fikrie, A., Kayamo, M., Bekele, H.: Malaria prevention practices and associated factors among households of Hawassa City Administration, Southern Ethiopia, 2020. PLoS ONE 16, e0250981 (2021). https://doi.org/10.1371/JOURNAL.PONE.0250981
Brown, B.J., et al.: Data-driven malaria prevalence prediction in large densely populated urban holoendemic sub-Saharan West Africa. Sci. Reports 101(10), 1–17 (2020 ). https://doi.org/10.1038/s41598-020-72575-6
Okagbue, H.I., Oguntunde, P.E., Obasi, E.C.M., Adamu, P.I., Opanuga, A.A.: Diagnosing malaria from some symptoms: a machine learning approach and public health implications. Health Technol. (Berl.) 11, 23–37 (2021). https://doi.org/10.1007/S12553-020-00488-5/TABLES/9
Mwanga, E.P., et al.: Using mid-infrared spectroscopy and supervised machine-learning to identify vertebrate blood meals in the malaria vector, Anopheles arabiensis. Malar J. 18, 1–9 (2019). https://doi.org/10.1186/S12936-019-2822-Y/FIGURES/7
Martineau, P., et al.: Predicting malaria outbreaks from sea surface temperature variability up to 9 months ahead in Limpopo, South Africa, using machine learning. Front. Public Heal. 10, 962377 (2022). https://doi.org/10.3389/FPUBH.2022.962377/FULL
Harvey, D., Valkenburg, W., Amara, A.: Predicting malaria epidemics in Burkina Faso with machine learning. PLoS ONE 16, e0253302 (2021). https://doi.org/10.1371/JOURNAL.PONE.0253302
Nkiruka, O., Prasad, R., Clement, O.: Prediction of malaria incidence using climate variability and machine learning. Inform. Med Unlocked 22, 100508 (2021)
Shamseer, L., et al.: Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: elaboration and explanation. BMJ 349 (2015). https://doi.org/10.1136/BMJ.G7647
Kalipe, G., Gautham, V., Behera, R.K.: Predicting malarial outbreak using machine learning and deep learning approach: a review and analysis. In: Proceedings of 2018 International Conference on Information Technologies, ICIT 2018 2018:33–8. https://doi.org/10.1109/ICIT.2018.00019
Yadav, S.S., Kadam, V.J., Jadhav, S.M., Jagtap, S., Pathak, P.R.: Machine learning based malaria prediction using clinical findings. In: 2021 International Conference on Emerging Smart Computing and Informatics, ESCI 2021, pp. 216–222 (2021). https://doi.org/10.1109/ESCI50559.2021.9396850
Mohapatra, P., Tripathi, N.K., Pal, I., Shrestha, S.: Determining suitable machine learning classifier technique for prediction of malaria incidents attributed to climate of Odisha (2021). https://doi.org/10.1080/09603123.2021.1905782
Lee, Y.W., Choi, J.W., Shin, E.H.: Machine learning model for predicting malaria using clinical information. Comput. Biol. Med. 129, 104151 (2021)
Muhammad, B., Varol, A.: A symptom-based machine learning model for malaria diagnosis in Nigeria. In: 9th International Symposium on Digital Forensics and Security, ISDFS 2021 (2021). https://doi.org/10.1109/ISDFS52919.2021.9486315
Zacarias, O.P., Bostrom, H.: Comparing support vector regression and random forests for predicting malaria incidence in Mozambique. In: 2013 International Conference on Advances in ICT for Emerging Regions (ICTer 2013) - Conference Proceedings, pp. 217–21 (2013). https://doi.org/10.1109/ICTER.2013.6761181
Mbunge, E., Millham, R.C., Sibiya, M.N., Takavarasha, S.: Application of machine learning models to predict malaria using malaria cases and environmental risk factors. In: 2022 Conference on Information Communications Technology and Society, ICTAS 2022 - Proceedings (2022). https://doi.org/10.1109/ICTAS53252.2022.9744657
Zafar, A., et al.: Machine learning-based risk factor analysis and prevalence prediction of intestinal parasitic infections using epidemiological survey data. PLoS Negl. Trop. Dis. 16, e0010517 (2022). https://doi.org/10.1371/JOURNAL.PNTD.0010517
Masinde, M.: Africa’s Malaria epidemic predictor: application of machine learning on Malaria incidence and climate data. In: ACM International Conference Proceeding Series, pp. 29–37 (2020). https://doi.org/10.1145/3388142.3388158
Dukuzumuremyi, A.: Machine learning based prediction of malaria outbreak using environment data in Rwanda (2020)
Adamu, Y.A.: Malaria prediction model using machine learning algorithms. Turkish J. Comput. Math. Educ. 12, 7488–7496 (2021). https://doi.org/10.17762/TURCOMAT.V12I10.5655
Phoobane, P., Masinde, M., Botai, J.: Prediction model for malaria: an ensemble of machine learning and hydrological drought indices, vol. 216. LNNS, pp. 569–584 (2022). https://doi.org/10.1007/978-981-16-1781-2_51/COVER
Iradukunda, O., et al.: Malaria disease prediction based on machine learning. In: IEEE International Conference on Signal, Information and Data Processing 2019 (2019). https://doi.org/10.1109/ICSIDP47821.2019.9173011
Mbunge, E., et al.: predicting student dropout in massive open online courses using deep learning models - a systematic review, vol. 503. LNNS, pp. 212–31 (2022). https://doi.org/10.1007/978-3-031-09073-8_20/COVER
Mariki, M., Mkoba, E., Mduma, N.: Combining clinical symptoms and patient features for Malaria diagnosis. Mach. Learn. Appr. (2022). https://doi.org/10.1080/08839514.2022.2031826
Mbunge E., et al.: Predicting diarrhoea among children under five years using machine learning techniques, vol. 502. LNNS, pp. 94–109 (2022). https://doi.org/10.1007/978-3-031-09076-9_9/COVER
Akinnuwesi, B.A., et al.: Application of support vector machine algorithm for early differential diagnosis of prostate cancer. Data Sci. Manag. (2022). https://doi.org/10.1016/J.DSM.2022.10.001
Zinszer, K., et al.: A scoping review of malaria forecasting: past work and future directions. BMJ Open 2, e001992 (2012). https://doi.org/10.1136/BMJOPEN-2012-001992
Golumbeanu, M., et al.: Leveraging mathematical models of disease dynamics and machine learning to improve development of novel malaria interventions. Infect. Dis. Poverty 11, 1–17 (2022). https://doi.org/10.1186/S40249-022-00981-1/FIGURES/6
Sharma, R.K., Thakor, H.G., Saha, K.B., Sonal, G.S., Dhariwal, A.C., Singh, N.: Malaria situation in India with special reference to tribal areas. Indian J. Med. Res. 141, 537 (2015). https://doi.org/10.4103/0971-5916.159510
Sudheer, C., et al.: a support vector machine-firefly algorithm based forecasting model to determine malaria transmission. Neurocomputing 129, 279–288 (2014). https://doi.org/10.1016/J.NEUCOM.2013.09.030
Wang, M., et al.: A novel model for malaria prediction based on ensemble algorithms. PLoS ONE 14, e0226910 (2019). https://doi.org/10.1371/JOURNAL.PONE.0226910
Zhang, H., Guo, J., Li, H., Guan, Y.: Machine learning for artemisinin resistance in malaria treatment across in vivo-in vitro platforms. IScience 25, 103910 (2022). https://doi.org/10.1016/J.ISCI.2022.103910
Pourhomayoun, M., Shakibi, M.: Predicting mortality risk in patients with COVID-19 using machine learning to help medical decision-making. Smart Heal 20, 100178 (2021). https://doi.org/10.1016/J.SMHL.2020.100178
Buczak, A.L., et al.: Fuzzy association rule mining and classification for the prediction of malaria in South Korea Standards, technology, and modeling. BMC Med. Inform. Decis. Mak. 15, 1–17 (2015). https://doi.org/10.1186/s12911-015-0170-6
Sornsuwit, P., Jaiyen, S.: A New Hybrid Machine Learning for Cybersecurity Threat Detection Based on Adaptive Boosting, vol. 33, pp. 462–82 (2019). https://doi.org/10.1080/08839514.2019.1582861
Walker, K.W., Jiang, Z.: Application of adaptive boosting (AdaBoost) in demand-driven acquisition (DDA) prediction: A machine-learning approach. J. Acad. Librariansh 45, 203–212 (2019). https://doi.org/10.1016/J.ACALIB.2019.02.013
Feng, D.C., et al.: Machine learning-based compressive strength prediction for concrete: An adaptive boosting approach. Constr. Build. Mater. 230, 117000 (2020). https://doi.org/10.1016/J.CONBUILDMAT.2019.117000
Bui. Q.-T., Nguyen, Q.,-H., Pham, V.M., Pham, M.H., Tran, A.T.: Understanding spatial variations of malaria in Vietnam using remotely sensed data integrated into GIS and machine learning classifiers, vol. 34, pp. 1300–1314 (2018). https://doi.org/10.1080/10106049.2018.1478890
Fashoto, S.G., Mbunge, E., Ogunleye, G., den Burg, J.V.: Implementation of machine learning for predicting maize crop yields using multiple linear regression and backward elimination/Stephen Gbenga Fashoto (2021)
Uddin. S., Khan, A., Hossain, M.E., Moni, M.A.: Comparing different supervised machine learning algorithms for disease prediction. BMC Med. Informat. Decis. Mak. 191(19), 1–16 (2019). https://doi.org/10.1186/S12911-019-1004-8
Cunningham, P., Delany, S.J.: k-Nearest neighbour classifiers - a tutorial. ACM Comput. Surv. 54 (2021). https://doi.org/10.1145/3459665
Grampurohit S, Sagarnal C. Disease prediction using machine learning algorithms. 2020 Int Conf Emerg Technol INCET 2020 2020. https://doi.org/10.1109/INCET49848.2020.9154130
Chingombe. I., et al.: Predicting HIV status among men who have sex with men in Bulawayo & Harare, Zimbabwe using bio-behavioural data.In: Recurrent Neural Networks, and Machine Learning Techniques. Trop Med Infect Dis 2022, vol. 7, p. 231 (2022). https://doi.org/10.3390/TROPICALMED7090231
Sokolova, M., Lapalme, G.: A systematic analysis of performance measures for classification tasks. Inf. Process. Manag. 45, 427–437 (2009). https://doi.org/10.1016/J.IPM.2009.03.002
Lahmiri, S., Dawson, D.A., Shmuel, A.: Performance of machine learning methods in diagnosing Parkinson’s disease based on dysphonia measures. Biomed. Eng. Lett. 8, 29–39 (2018). https://doi.org/10.1007/S13534-017-0051-2/FIGURES/2
Seliya, N., Khoshgoftaar, T.M., Van Hulse, J.: A study on the relationships of classifier performance metrics. In: Proceedings of International Conference on Tools with Artificial Intelligence (ICTAI 2009), pp. 59–66 (2009). https://doi.org/10.1109/ICTAI.2009.25
Erickson, B.J., Kitamura, F.: Magician’s corner: 9. performance metrics for machine learning models. Radiol. Artif. Intell. 3 (2021). https://doi.org/10.1148/RYAI.2021200126
Alaa Khaleel, F., Al-Bakry, A.M.: Diagnosis of diabetes using machine learning algorithms. Mater Today Proc. (2021). https://doi.org/10.1016/J.MATPR.2021.07.196
Gonzalez-Cuautle, D., et al.: Synthetic minority oversampling technique for optimizing classification tasks in Botnet and intrusion-detection-system datasets. Appl. Sci. 10, 794 (2020). https://doi.org/10.3390/APP10030794
Gunda, R., Chimbari, M.J., Shamu, S., Sartorius, B., Mukaratirwa, S.: Malaria incidence trends and their association with climatic variables in rural Gwanda, Zimbabwe, 2005–2015. Malar. J. 161, 1–13 (2017). https://doi.org/10.1186/S12936-017-2036-0
Gong, Y.F., Zhu, L.Q., Li, Y.L., Zhang, L.J., Xue, J.B., Xia, S., et al.: Identification of the high-risk area for schistosomiasis transmission in China based on information value and machine learning: a newly data-driven modeling attempt. Infect Dis Poverty 10, 1–11 (2021). https://doi.org/10.1186/S40249-021-00874-9/FIGURES/3
Manyangadze, T., Mavhura, E., Mudavanhu, C., Pedzisai, E.: An exploratory analysis of the spatial variation of malaria cases and associated household socio-economic factors in flood-prone areas of Mbire district, Zimbabwe. GeoJournal, 1–16 (2021). https://doi.org/10.1007/S10708-021-10505-3/FIGURES/3
Zinszer, K., et al.: Forecasting malaria in a highly endemic country using environmental and clinical predictors. Malar J. 14, 1–9 (2015). https://doi.org/10.1186/S12936-015-0758-4/FIGURES/4
Chekol, B.E., Hagras, H.: Employing machine learning techniques for the Malaria epidemic prediction in Ethiopia. In: 2018 10th Computer Science and Electronic Engineering Conference (CEEC): Conference Proceedings, pp. 89–94 (2019). https://doi.org/10.1109/CEEC.2018.8674210
Seo, J.H., Kim, Y.H.: Machine-learning approach to optimize smote ratio in class imbalance dataset for intrusion detection. Comput. Intell. Neurosci. 2018 (2018). https://doi.org/10.1155/2018/9704672
Liu, X.Y., Wu, J., Zhou, Z.H.: Exploratory undersampling for class-imbalance learning. IEEE Trans. Syst. Man Cybern. Part B Cybern. 39, 539–550 (2009). https://doi.org/10.1109/TSMCB.2008.2007853
Guo, X., Yin, Y., Dong, C., Yang, G., Zhou. G.: On the class imbalance problem. In: Proceedings - 4th International Conference on Natural Computation, ICNC 2008, vol. 4, pp. 192–201 (2008). https://doi.org/10.1109/ICNC.2008.871
Zhu, T., Lin, Y., Liu, Y.: Synthetic minority oversampling technique for multiclass imbalance problems. Pattern Recognit. 72, 327–340 (2017). https://doi.org/10.1016/J.PATCOG.2017.07.024
Elreedy, D., Atiya, A.F.: A comprehensive analysis of synthetic minority oversampling technique (smote) for handling class imbalance. Inf. Sci. (Ny) 505, 32–64 (2019). https://doi.org/10.1016/J.INS.2019.07.070
Mfisimana, L.D., Nibayisabe, E., Badu, K., Niyukuri, D.: Exploring predictive frameworks for malaria in Burundi. Infect. Dis. Model 7, 33–44 (2022). https://doi.org/10.1016/J.IDM.2022.03.003
Sow, B., Mukhtar, H., Ahmad, H.F., Suguri, H.: Assessing the relative importance of social determinants of health in malaria and anemia classification based on machine learning techniques, vol. 45, pp. 229–41 (2019). https://doi.org/10.1080/17538157.2019.1582056
Chingombe, I., et al.: Predicting HIV Status using machine learning techniques and bio-behavioural data from the zimbabwe population-based hiv impact assessment (ZIMPHIA15–16). In: Silhavy, R. (eds.) Artificial Intelligence Trends in Systems. CSOC 2022. LNNS, vol 502. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-09076-9_24
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Mbunge, E., Milham, R.C., Sibiya, M.N., Takavarasha, S. (2023). Machine Learning Techniques for Predicting Malaria: Unpacking Emerging Challenges and Opportunities for Tackling Malaria in Sub-saharan Africa. In: Silhavy, R., Silhavy, P. (eds) Artificial Intelligence Application in Networks and Systems. CSOC 2023. Lecture Notes in Networks and Systems, vol 724. Springer, Cham. https://doi.org/10.1007/978-3-031-35314-7_30
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