Combining Clinical Symptoms and Patient Features for Malaria Diagnosis: Machine Learning Approach

References

• Abba, K., J. J. Deeks, P. L. Olliaro, C.-M. Naing, S. M. Jackson, Y. Takwoingi, S. Donegan, and P. Garner. 2011. Rapid diagnostic tests for diagnosing uncomplicated P. falciparum malaria in endemic countries. Cochrane Database of Systematic Reviews. doi:10.1002/14651858.CD008122.PUB2.
   Web of Science ®Google Scholar
• Attinsounon, C. A., Y. Sissinto, E. Avokpaho, A. Alassani, M. Sanni, and M. Zannou. 2019. Self-medication practice against malaria and associated factors in the City of Parakou in Northern Benin: Results of a population survey in 2017. Advances in Infectious Diseases 9 (3):263–2031. doi:10.4236/aid.2019.93020.
   Google Scholar
• Azar, A. T., H. I. Elshazly, A. E. Hassanien, and A. M. Elkorany. 2014. A random forest classifier for lymph diseases. Computer Methods and Programs in Biomedicine 113 (2):465–73. doi:10.1016/J.CMPB.2013.11.004.
   PubMed Web of Science ®Google Scholar
• Baltzell, K., T. B. Kortz, E. Scarr, A. Blair, A. Mguntha, G. Bandawe, E. Schell, and S. Rankin. 2019. ’Not all fevers are Malaria’: A mixed methods study of non-malarial fever management in Rural Southern Malawi. Rural and Remote Health 19 (2). doi: 10.22605/RRH4818.
   PubMed Web of Science ®Google Scholar
• Bbosa, F., R. Wesonga, and P. Jehopio. 2016. Clinical malaria diagnosis: Rule-based classification statistical prototype. SpringerPlus 5 (1):1–14. doi:10.1186/S40064-016-2628-0/TABLES/7.
   PubMedGoogle Scholar
• Bibin, D., M. S. Nair, and P. Punitha. 2017. Malaria parasite detection from peripheral blood smear images using deep belief networks. IEEE Access 5:9099–108. doi:10.1109/ACCESS.2017.2705642.
   Web of Science ®Google Scholar
• Bria, Y. P., C. H. Yeh, and S. Bedingfield. 2021. Significant symptoms and nonsymptom-related factors for malaria diagnosis in endemic regions of Indonesia. International Journal of Infectious Diseases 103:194–200. doi:10.1016/j.ijid.2020.11.177.
   PubMed Web of Science ®Google Scholar
• Brodersen, K. H., F. Haiss, C. S. Ong, F. Jung, M. Tittgemeyer, J. M. Buhmann, B. Weber, and K. E. Stephan. 2011. Model-based feature construction for multivariate decoding. NeuroImage 56 (2):601–15. doi:10.1016/j.neuroimage.2010.04.036.
   PubMed Web of Science ®Google Scholar
• Brown, B. J., P. Manescu, A. A. Przybylski, F. Caccioli, G. Oyinloye, M. Elmi, M. J. Shaw, V. Pawar, R. Claveau, J. Shawe-Taylor, et al. 2020. Data-driven malaria prevalence prediction in large densely populated urban holoendemic sub-Saharan West Africa. Scientific Reports 10 (1). doi: 10.1038/S41598-020-72575-6.
   Web of Science ®Google Scholar
• Chandramohan, D., I. Carneiro, A. Kavishwar, R. Brugha, V. Desai, and B. Greenwood. 2001. A clinical algorithm for the diagnosis of malaria: Results of an evaluation in an area of low endemicity. Tropical Medicine and International Health 6 (7):505–10. doi:10.1046/j.1365-3156.2001.00739.x.
   PubMed Web of Science ®Google Scholar
• Chen, P.-H. C., Y. Liu, and L. Peng. 2019. How to develop machine learning models for healthcare. Nature Materials 18 (5):410–14. doi:10.1038/s41563-019-0345-0.
   PubMed Web of Science ®Google Scholar
• Chipwaza, B., J. P. Mugasa, I. Mayumana, M. Amuri, C. Makungu, and P. S. Gwakisa. 2014. Self-medication with anti-malarials is a common practice in rural communities of Kilosa district in Tanzania despite the reported decline of malaria. Malaria Journal 13 (1):252. doi:10.1186/1475-2875-13-252.
   PubMedGoogle Scholar
• Crump, J. A., P. N. Newton, S. J. Baird, and Y. Lubell. 2017. Febrile illness in adolescents and adults. Disease Control Priorities, Third Edition (Volume 6): Major Infectious Diseases 365–85. doi:10.1596/978-1-4648-0524-0_CH14.
   Google Scholar
• D’Acremont, V., C. L. H. M. D. M. M. T. B. G, H. Mshinda, D. Mtasiwa, M. Tanner, and B. Genton. 2009. Time to move from presumptive malaria treatment to laboratory-confirmed diagnosis and treatment in African children with fever. PLoS Medicine 6 (1):e252. doi:10.1371/journal.pmed.0050252.
   PubMed Web of Science ®Google Scholar
• Das, D. K., M. Ghosh, M. Pal, A. K. Maiti, and C. Chakraborty. 2013. Machine learning approach for automated screening of malaria parasite using light microscopic images. Micron 45:97–106. doi:10.1016/J.MICRON.2012.11.002.
   PubMed Web of Science ®Google Scholar
• Davenport, T., and R. Kalakota. 2019. The potential for artificial intelligence in healthcare. Future Healthcare Journal 6 (2):94–98. doi:10.7861/futurehosp.6-2-94.
   PubMedGoogle Scholar
• DB, D., F. P, and K. N. 2018. Machine learning approaches for clinical psychology and psychiatry. Annual Review of Clinical Psychology 14 (1):91–118. doi:10.1146/ANNUREV-CLINPSY-032816-045037.
   PubMedGoogle Scholar
• Debora, C. K., and E. Moses. 2017. Self-medication practices and predictors for self-medication with antibiotics and anti-malarials among community in Mbeya City, Tanzania. Tanzania Journal of Health Research 19 (4). doi: 10.4314/THRB.V19I4.
   Google Scholar
• Dharap, P., and S. Raimbault. 2020. Performance evaluation of machine learning-based infectious screening flags on the HORIBA Medical Yumizen H550 Haematology Analyzer for vivax malaria and dengue fever. Malaria Journal 19 (1):1–10. doi:10.1186/S12936-020-03502-3.
   PubMed Web of Science ®Google Scholar
• Fatima, M., and M. Pasha. 2017. Survey of machine learning algorithms for disease diagnostic. Journal of Intelligent Learning Systems and Applications 9 (1):1–16. http://file.scirp.org/Html/.
   Google Scholar
• Femi Aminu, E., E. Onyebuchi Ogbonnia, and I. Shehi Shehu. 2016. A predictive symptoms-based system using support vector machines to enhanced classification accuracy of malaria and typhoid coinfection. International Journal of Mathematical Sciences and Computing 2 (4):54–66. doi:10.5815/ijmsc.2016.04.06.
   Google Scholar
• Ford, C. T., G. Alemayehu, K. Blackburn, K. Lopez, C. C. Dieng, E. Lo, L. Golassa, and D. Janies. 2020. Modeling plasmodium falciparum diagnostic test sensitivity using machine learning with histidine-rich Protein 2 variants. MedRxiv 20114785. doi:10.1101/2020.05.27.20114785.
   Google Scholar
• Fuhad, K. M. F., J. F. Tuba, M. R. A. Sarker, S. Momen, N. Mohammed, and T. Rahman. 2020a. Deep learning based automatic malaria parasite detection from blood smear and its smartphone based application. Diagnostics 10 (5):329. doi:10.3390/diagnostics10050329.
   Web of Science ®Google Scholar
• Fuhad, K. M. F., J. F. Tuba, M. R. A. Sarker, S. Momen, N. Mohammed, and T. Rahman. 2020b. Deep learning based automatic malaria parasite detection from blood smear and its smartphone based application. Diagnostics 10 (5):329. doi:10.3390/DIAGNOSTICS10050329.
   Web of Science ®Google Scholar
• Goodyer, L. 2015. Dengue fever and chikungunya: Identification in travellers. Clinical Pharmacist 7 (4). doi: 10.1211/cp.2015.20068429.
   Google Scholar
• Gosling, R. D., C. J. Drakeley, A. Mwita, and D. Chandramohan. 2008. Presumptive treatment of fever cases as malaria: Help or hindrance for malaria control? In Malaria Journal 7(1):132. BioMed Central. doi:10.1186/1475-2875-7-132.
   PubMedGoogle Scholar
• Graz, B., M. Willcox, T. Szeless, and A. Rougemont. 2011. Test and treat or presumptive treatment for malaria in high transmission situations? A reflection on the latest WHO guidelines. Malaria Journal 10 (1):1–8. doi:10.1186/1475-2875-10-136.
   PubMedGoogle Scholar
• Grobusch, M. P., and P. Schlagenhauf. 2019. 16 – Self-diagnosis and self-treatment of malaria by the traveler. In Travel Medicine 169–78. doi:10.1016/B978-0-323-54696-6.00016-1.
   Google Scholar
• Habib, P. T., A. M. Alsamman, S. E. Hassnein, G. A. Shereif, and A. Hamwieh. n.d. Assessment of machine learning algorithms for prediction of breast cancer malignancy based on mammogram numeric data. doi:10.1101/2020.01.08.20016949.
   Google Scholar
• Harvey, D., W. Valkenburg, A. Amara, and T. R. Gadekallu. 2021. Predicting malaria epidemics in Burkina Faso with machine learning. PLoS ONE 16 (6):e0253302. doi:10.1371/JOURNAL.PONE.0253302.
   PubMed Web of Science ®Google Scholar
• Hertz, J. T., D. B. Madut, R. A. Tesha, G. William, R. A. Simmons, S. W. Galson, V. P. Maro, J. A. Crump, and M. P. Rubach. 2019. Self-medication with non-prescribed pharmaceutical agents in an area of low malaria transmission in northern Tanzania: A community-based survey. Transactions of the Royal Society of Tropical Medicine and Hygiene 113 (4):183–88. doi:10.1093/trstmh/try138.
   PubMed Web of Science ®Google Scholar
• Isiguzo, C., J. Anyanti, C. Ujuju, E. Nwokolo, A. De La Cruz, E. Schatzkin, S. Modrek, D. Montagu, J. Liu, and H. D. F. H. Schallig. 2014. Presumptive treatment of malaria from formal and informal drug vendors in Nigeria. PLoS ONE 9 (10):e110361. doi:10.1371/journal.pone.0110361.
   PubMed Web of Science ®Google Scholar
• Iyer, A., J. S, and R. Sumbaly. 2015. Diagnosis of diabetes using classification mining techniques. International Journal of Data Mining & Knowledge Management Process 5 (1):01–14. http://www.aircconline.com/ijdkp/V5N1/5115ijdkp01.pdf.
   Google Scholar
• Jiang, Fei, Jiang, Yong, Zhi, Hui, Dong, Yi, Li, Hao, Ma, Sufeng, Wang, Yilong, Dong, Qiang, Shen, Haipeng, Wang, Yongjun. 01 12 2017. Artificial intelligence in healthcare: Past, present and future. Stroke and vascular neurology 2 (4): 230–243. 25 January 2022. doi:10.1136/SVN-2017-000101. https://pubmed.ncbi.nlm.nih.gov/29507784/
   PubMedGoogle Scholar
• Kazaura, M. R. 2017. Level and correlates of self-medication among adults in a rural setting of mainland Tanzania. Indian Journal of Pharmaceutical Sciences 79 (3):451–57. doi:10.4172/pharmaceutical-sciences.1000248.
   Web of Science ®Google Scholar
• Khare, A., M. Jeon, I. K. Sethi, and B. Xu. 2017. Machine learning theory and applications for healthcare. In Journal of Healthcare Engineering 2017:1–2. Hindawi Limited. doi:10.1155/2017/5263570.
   Web of Science ®Google Scholar
• Kim, J. K., Y. J. Choo, and M. C. Chang. 2021. Prediction of motor function in stroke patients using machine learning algorithm: Development of practical models. Journal of Stroke and Cerebrovascular Diseases 30 (8):105856. doi:10.1016/J.JSTROKECEREBROVASDIS.2021.105856.
   PubMed Web of Science ®Google Scholar
• Krishnani, D., A. Kumari, A. Dewangan, A. Singh, and N. S. Naik. 2019. Prediction of coronary heart disease using supervised machine learning algorithms. IEEE Region 10 Annual International Conference, Proceedings/TENCON, 17 20 October 2019. Kochi, India, 367–72, October. doi:10.1109/TENCON.2019.8929434. https://ieeexplore.ieee.org/document/8929434
   Google Scholar
• Lee, Y. W., J. W. Choi, and E. H. Shin. 2021. Machine learning model for predicting malaria using clinical information. Computers in Biology and Medicine 129:104151. doi:10.1016/J.COMPBIOMED.2020.104151.
   PubMed Web of Science ®Google Scholar
• Liang, Z., A. Powell, I. Ersoy, M. Poostchi, K. Silamut, K. Palaniappan, P. Guo, M. A. Hossain, A. Sameer, and R. J. Maude, et al. 19 January 2017. CNN-based image analysis for malaria diagnosis. Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016, 15 - 18 December 2016. Shenzhen, China, 493–96. 25 January 2022. doi:10.1109/BIBM.2016.7822567. https://ieeexplore.ieee.org/document/7822567
   Google Scholar
• Madhu, G. 2020. Computer vision and machine learning approach for malaria diagnosis in thin blood smears from microscopic blood images, 191–209. Singapore: Springer. doi:10.1007/978-981-15-3689-2_8.
   Google Scholar
• Mariki, M., N. Mduma, and E. Mkoba. 2021. Feature selection approach to improve malaria diagnosis model for high and low endemic areas of Tanzania . doi:10.20944/PREPRINTS202111.0243.V1. https://www.preprints.org/manuscript/202111.0243/v1
   Google Scholar
• Masud, M., H. Alhumyani, S. S. Alshamrani, O. Cheikhrouhou, S. Ibrahim, G. Muhammad, M. S. Hossain, and M. Shorfuzzaman. 2020. Leveraging deep learning techniques for malaria parasite detection using mobile application. Wireless Communications and Mobile Computing 2020:1–15. doi:10.1155/2020/8895429.
   Web of Science ®Google Scholar
• Mboera, L. E. G., E. A. Makundi, and A. Y. Kitua. 2007. Uncertainty in malaria control in Tanzania: Crossroads and challenges for future interventions. https://www.ncbi.nlm.nih.gov/books/NBK1714/
   Google Scholar
• Menard, D., and A. Dondorp. 2017. Anti-malarial drug resistance: A threat to malaria elimination. Cold Spring Harbor Perspectives in Medicine 7 (7):1–24. doi:10.1101/cshperspect.a025619.
   Web of Science ®Google Scholar
• Metta, E., H. Haisma, F. Kessy, I. Hutter, and A. Bailey. 2014. “we have become doctors for ourselves”: Motives for malaria self-care among adults in southeastern Tanzania. Malaria Journal 13 (1):249. doi:10.1186/1475-2875-13-249.
   PubMedGoogle Scholar
• Michael, D., and S. P. Mkunde. 2017. The malaria testing and treatment landscape in mainland Tanzania, 2016. Malaria Journal 16 (1):1–15. doi:10.1186/S12936-017-1819-7.
   PubMedGoogle Scholar
• Moreno-Ibarra, M.-A., Y. Villuendas-Rey, M. D. Lytras, C. Yáñez-Márquez, and J.-C. Salgado-Ramírez. 2021. Classification of diseases using machine learning algorithms: A comparative study. Mathematics 9 (15):1817. doi:10.3390/MATH9151817.
   Web of Science ®Google Scholar
• Mpapalika, J. J., and N. Matowo. 2020. The application of Artificial Intelligence in the diagnosis and treatment of malaria in Tanzania. J Infectious Diseases Diagnosis 1. https://www.mdpi.
   Google Scholar
• MS, S., F. E, and C. J. 2020. Machine learning for brain stroke: A review. Journal of Stroke and Cerebrovascular Diseases : The Official Journal of National Stroke Association 29 (10). doi: 10.1016/J.JSTROKECEREBROVASDIS.2020.105162.
   Web of Science ®Google Scholar
• Muthumbi, A., A. Chaware, K. Kim, K. C. Zhou, P. C. Konda, R. Chen, B. Judkewitz, A. Erdmann, B. Kappes, and R. Horstmeyer. 2019. Learned sensing: Jointly optimized microscope hardware for accurate image classification. Biomedical Optics Express 10 (12):6351. doi:10.1364/boe.10.006351.
   PubMed Web of Science ®Google Scholar
• Mwai, L., E. Ochong, A. Abdirahman, S. M. Kiara, S. Ward, G. Kokwaro, P. Sasi, K. Marsh, S. Borrmann, M. MacKinnon, et al. 2009. Chloroquine resistance before and after its withdrawal in Kenya. Malaria Journal 8 (1):106. doi:10.1186/1475-2875-8-106.
   PubMedGoogle Scholar
• Mwanga, E. P., E. G. Minja, E. Mrimi, M. G. Jiménez, J. K. Swai, S. Abbasi, H. S. Ngowo, D. J. Siria, S. Mapua, C. Stica, et al. 2019b. Detection of malaria parasites in dried human blood spots using mid-infrared spectroscopy and logistic regression analysis. Malaria Journal 18 (1). doi: 10.1186/S12936-019-2982-9.
   Web of Science ®Google Scholar
• Mwanga, E. P., S. A. Mapua, D. J. Siria, H. S. Ngowo, F. Nangacha, J. Mgando, F. Baldini, M. González Jiménez, H. M. Ferguson, K. Wynne, et al. 2019a. Using mid-infrared spectroscopy and supervised machine-learning to identify vertebrate blood meals in the malaria vector, Anopheles arabiensis. Malaria Journal 18 (1). doi: 10.1186/s12936-019-2822-y.
   Google Scholar
• Mwita, S., O. Meja, D. Katabalo, and C. Richard. 2019. Magnitude and factors associated with anti-malarial self-medication practice among residents of Kasulu Town Council, Kigoma-Tanzania. African Health Sciences 19 (3):2457–61. doi:10.4314/ahs.v19i3.20.
   PubMed Web of Science ®Google Scholar
• Nadjm, B., B. Amos, G. Mtove, J. Ostermann, S. Chonya, H. Wangai, J. Kimera, W. Msuya, F. Mtei, D. Dekker, et al. 2010. WHO guidelines for antimicrobial treatment in children admitted to hospital in an area of intense Plasmodium falciparum transmission: Prospective study. BMJ (Online) 340 (7751):848. doi:10.1136/BMJ.C1350.
   Google Scholar
• Ngasala, B., M. Mubi, M. Warsame, M. G. Petzold, A. Y. Massele, L. L. Gustafsson, G. Tomson, Z. Premji, and A. Bjorkman. 2008. Impact of training in clinical and microscopy diagnosis of childhood malaria on anti-malarial drug prescription and health outcome at primary health care level in Tanzania: A randomized controlled trial. Malaria Journal 7 (1):199. doi:10.1186/1475-2875-7-199.
   PubMedGoogle Scholar
• Nkumama, I. N., W. P. O’Meara, and F. H. A. Osier. 2017. Changes in malaria epidemiology in Africa and new challenges for elimination. Trends in Parasitology 33(2):128–40. Elsevier Ltd. doi:10.1016/j.pt.2016.11.006.
   PubMed Web of Science ®Google Scholar
• Oguntimilehin, A. n.d. A mobile malaria fever clinical diagnosis system based on Non-Nested Generalized Exemplar (NNGE). International Journal of Emerging Trends in Engineering Research 8 (2):259–64. Accessed January 8, 2022. https://www.academia.edu/43021750/A_Mobile_Malaria_Fever_Clinical_Diagnosis_System_Based_on_Non_Nested_Generalized_Exemplar_NNGE_
   Google Scholar
• Patil, P., N. Yaligar, and S. Meena. 2018. Comparision of performance of classifiers - SVM, RF and ANN in potato blight disease detection using leaf images. 2017 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2017, 14-16 Dec. 2017. 25 January 2022. Coimbatore, India. doi:10.1109/ICCIC.2017.8524301. https://ieeexplore.ieee.org/document/8524301
   Google Scholar
• Pillay, E., S. Khodaiji, B. C. Bezuidenhout, M. Litshie, and T. L. Coetzer. 2019. Evaluation of automated malaria diagnosis using the Sysmex XN-30 analyser in a clinical setting. Malaria Journal 18 (1):15. doi:10.1186/s12936-019-2655-8.
   PubMedGoogle Scholar
• Poostchi, M., K. Silamut, R. J. Maude, S. Jaeger, and G. Thoma. 2018. Image analysis and machine learning for detecting malaria. In Translational Research, vol. 194, 36–55. 25 January 2022. doi:10.1016/j.trsl.2017.12.004.
   PubMed Web of Science ®Google Scholar
• Priyadarshini, R., N. Dash, and R. Mishra. 2014. A novel approach to predict diabetes mellitus using modified extreme learning machine. 2014 International Conference on Electronics and Communication Systems, ICECS 2014, 13-14 Feb. 2014. 25 January 2022. Coimbatore, India. doi:10.1109/ECS.2014.6892740. https://ieeexplore.ieee.org/document/6892740?reload=true
   Google Scholar
• Rajaraman, S., S. Jaeger, and S. K. Antani. 2019. Performance evaluation of deep neural ensembles toward malaria parasite detection in thin-blood smear images. PeerJ 7:e6977. doi:10.7717/peerj.6977.
   PubMed Web of Science ®Google Scholar
• Rajaraman, S., S. K. Antani, M. Poostchi, K. Silamut, M. A. Hossain, R. J. Maude, S. Jaeger, and G. R. Thoma. 2018. Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images. PeerJ 2018(4). doi:10.7717/peerj.4568
   Google Scholar
• Rao, A. R., and B. S. Renuka. 2020. A machine learning approach to predict thyroid disease at early stages of diagnosis. 2020 IEEE International Conference for Innovation in Technology, INOCON 2020, 6-8 Nov. 2020. 25 January 2022. Bangluru, India. doi:10.1109/INOCON50539.2020.9298252. https://ieeexplore.ieee.org/document/9298252
   Google Scholar
• Ravalji, R., N. Shah, and M. Nai. 2020. Malaria disease detection using machine learning. International Research Journal of Engineering and Technology (IRJET) 7 (12): 1180–1188. https://www.irjet.net/archives/V7/i12/IRJET-V7I12212.pdf
   Google Scholar
• Saranya, G., and A. Pravin. 2020. A comprehensive study on disease risk predictions in machine learning related papers A comprehensive study on disease risk predictions in machine learning. International Journal of Electrical and Computer Engineering (IJECE) International Journal of Electrical and Computer Engineering (IJECE) International Journal of Electrical and Computer Engineering (IJECE) 10 (4):4217–25. doi:10.11591/ijece.v10i4.pp4217-4225.
   Google Scholar
• Shailaja, K., B. Seetharamulu, and M. A. Jabbar. 2018. Machine learning in healthcare: A review. Proceedings of the 2nd International Conference on Electronics, Communication and Aerospace Technology, ICECA 2018, 29-31 March 2018. Coimbatore, India, 910–14. 25 January 2022. doi:10.1109/ICECA.2018.8474918. https://ieeexplore.ieee.org/document/8474918
   Google Scholar
• Shekalaghe, S., M. Cancino, C. Mavere, O. Juma, A. Mohammed, S. Abdulla, and S. Ferro. 2013. Clinical performance of an automated reader in interpreting malaria rapid diagnostic tests in Tanzania. Malaria Journal 12 (1):1–9. doi:10.1186/1475-2875-12-141.
   PubMedGoogle Scholar
• Sidey-Gibbons, J. A. M., and C. J. Sidey-Gibbons. 2019. Machine learning in medicine: A practical introduction. BMC Medical Research Methodology 19 (1):64. doi:10.1186/s12874-019-0681-4.
   PubMedGoogle Scholar
• Sigonda-Ndomondo, M., R. Mbwasi, R. Shirima, N. Heltzer, and M. Clark. 2004. Accredited drug dispensing outlets: improving access to quality drugs and services in rural and periurban areas with few or no pharmacies. Second International Conference on Improving Use of Medicines, 30 March - 2 April 2004. Chiang Mai, Thailand.
   Google Scholar
• Swaminathan, S., K. Qirko, T. Smith, E. Corcoran, N. G. Wysham, G. Bazaz, G. Kappel, A. N. Gerber, and M. Milanese. 2017. A machine learning approach to triaging patients with chronic obstructive pulmonary disease. PLOS ONE 12 (11):e0188532. doi:10.1371/JOURNAL.PONE.0188532.
   PubMed Web of Science ®Google Scholar
• Triantafyllidis, A. K., and A. Tsanas. 2019. Applications of machine learning in real-life digital health interventions: Review of the literature. In Journal of Medical Internet Research 21 (4):e12286. doi:10.2196/12286.
   PubMed Web of Science ®Google Scholar
• Uddin, S., A. Khan, M. E. Hossain, and M. A. Moni. 2019. Comparing different supervised machine learning algorithms for disease prediction. BMC Medical Informatics and Decision Making 19 (1):1–16/281. 25 January 2022. doi:10.1186/S12911-019-1004-8. https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-019-1004-8
   PubMed Web of Science ®Google Scholar
• Ullah, R., S. Khan, H. Ali, I. I. Chaudhary, M. Bilal, and I. Ahmad. 2019. A comparative study of machine learning classifiers for risk prediction of asthma disease. Photodiagnosis and Photodynamic Therapy 28:292–96. doi:10.1016/J.PDPDT.2019.10.011.
   PubMed Web of Science ®Google Scholar
• UM, C. 2016. Malaria treatment in children based on presumptive diagnosis: A make or mar? Pediatric Infectious Diseases: Open Access 1 (2). doi: 10.21767/2573-0282.100006.
   Google Scholar
• van Driel, N. 2020. Automating malaria diagnosis: a machine learning approach: Erythrocyte segmentation and parasite identification in thin blood smear microscopy images using convolutional neural networks. https://repository.tudelft.nl/islandora/object/uuid%3A2db4839e-7c68-4536-895e-13e51821c9a9
   Google Scholar
• Wang, D., P. Chaki, Y. Mlacha, T. Gavana, M. G. Michael, R. Khatibu, J. Feng, Z. B. Zhou, K. M. Lin, S. Xia, et al. 2019. Application of community-based and integrated strategy to reduce malaria disease burden in southern Tanzania: The study protocol of China-UK-Tanzania pilot project on malaria control. Infectious Diseases of Poverty 8 (1):1–6. doi:10.1186/s40249-018-0507-3.
   PubMedGoogle Scholar
• WHO-Guidelines. 2015. For the treatment of malaria guidelines. www.who.int
   Google Scholar
• WHO. 2015. WHO | Guidelines for the treatment of malaria - 3rd Edition. Geneva: World Health Organization.
   Google Scholar
• WHO. 2019. World Malaria Report 2019. Geneva: World Health Organization. https://www.who.int/publications/i/item/9789241565721
   Google Scholar
• WHO. 2020. World malaria report 2020. Geneva: World Health Organization. https://www.who.int/publications/i/item/9789240015791
   Google Scholar
• WHO. 2021. INTRODUCTION - WHO guidelines for malaria - NCBI Bookshelf. Geneva: NCBI. https://www.ncbi.nlm.nih.gov/books/NBK568497/.
   Google Scholar
• Yadav, S. S., V. J. Kadam, S. M. Jadhav, S. Jagtap, and P. R. Pathak. 2021. Machine learning based malaria prediction using clinical findings. 2021 International Conference on Emerging Smart Computing and Informatics, ESCI 2021, 5-7 March 2021, Pune, India, 216–22. 25 January 2022. doi:10.1109/ESCI50559.2021.9396850. https://ieeexplore.ieee.org/document/9396850
   Google Scholar
• Yang, F., H. Yu, K. Silamut, R. J. Maude, S. Jaeger, and S. Antani. 2019. Smartphone-supported malaria diagnosis based on deep learning. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11861 LNCS 73–80. doi:10.1007/978-3-030-32692-0_9.
   Google Scholar