Lassa Fever Predictive Model Using Machine Learning Techniques
Abstract
Lassa Fever, a severe hemorrhagic fever caused by the Lassa virus, is notably prevalent in West Africa and can be fatal if not managed properly. Effective outbreak control and mitigation rely heavily on early detection. This study introduces a predictive model for forecasting Lassa Fever using machine learning techniques. The research employs an extensive dataset that includes epidemiological and environmental data collected over multiple years to construct a reliable predictive model. Several machine learning algorithms, such as Random Forest, Support Vector Machines (SVM), and Gradient Boosting, are utilized to analyze historical data and predict potential Lassa Fever outbreaks. To improve model accuracy and minimize false positives, feature engineering and data preprocessing methods are applied. The dataset was divided into 80% for training and 20% for testing, with various algorithms—including SVM, K-Nearest Neighbor, Naïve Bayes, and Logistic Regression—tested to build the predictive models. The performance of these models was assessed using metrics like accuracy, precision, recall, and F1 score. The SVM model demonstrated the highest performance, achieving accuracy, precision, and F1 scores of 90%, 91%, and 96%, respectively. Consequently, SVM was selected as the preferred algorithm for this research. The study recommends the use of this model in Nigeria's medical industry to enhance diagnostic speed and reduce medical errors in healthcare settings.
Keywords
Lassa FeverPredictive ModelMachine LearningSupport
References
Adeboye, K.R., & Haruna M. (2015). A mathematical model for the transmission and control of malaria and typhoid co-infection using SIRS approach. Research Journal of Mathematics, 2(2), 1-24. Adedeji, A., &Smith, S. (2016). The influence of the Nigerian newspaper on Lassa fever reportage. International Journal of Community Health, 11, 76–86. Abiola, H.M., Alaba, A.A.,& Joy, D.L. (2017).Expert System for Lassa Fever Diagnosis Using Rule-Based Approach.Annals. Computer Science Series, 15(2), 68-74. Abiola, O.O., Idowu, P.A, &Akinyemi, I.O. (2020). Development of a Fuzzy Logic Based Predictive Model for The Risk of Lassa Fever. Journal of Vector Borne Diseases, 3(11), 28-32. Ajenaghughrure, I.B., Sujatha, P.,&Akazue, M.I. (2017). Fuzzy Based Multi-Fever Symptom Classifier Diagnosis Model. International Journal of Informational Technology&Computer Science,9(10), 13-28. Aminu, E.F., Abiodun, A.A., Isah, O.R., Iliyasu, A., Isah, A.O., & Hussaini, A.Z. (2018). A Diagnosis System for Lassa Fever and Related Ailments Using Fuzzy Logic. Journal of Science, Technology, Mathematics and Education (JOSTMED), 4(2), 18-30. Amarosa, V., MacNeil, A., McConnell, R., Patel, A., Dillon, K.E., Hamilton, K., Erickson, B.R., Campbell, S., Knust, B., Cannon, D.,Miller, D., Manning, C., Rollin, P.E., &Nichol, S.T.(2010). Imported Lassa fever, Pennsylvania, USA.Journal of Emergency Infectious Diseases, 16, 1598– 1600. Alile, S.O. (2020). Application of Bayesian Networks in early diagnosis of cerebral malaria and mosquito-borne diseases based on observed symptoms. International Journal of Scientific Research &Multidisciplinary Studies, 6(6), 1-14. Batta M., (2018). Machine Learning Algorithms - A Review. International Journal of Science and Research, 6(2), 11-14. Djam, X.Y., Wajiga, G. M., Kimbi, Y.H., &BlamahN.V. (2011). A Fuzzy Expert System for the Management of Malaria. International Journal of Pure Applied Science and Technology, 5(2), 84-108. Egwali, A., &Obi, J.C. (2015).An Adaptive Neuro-Fuzzy Inference System for Diagnosis of EHF. Pacific Journal ofScience & Technology, 16(1), 251-261. Eze, K.C., Salami, T.A., Eze I.C., Pogoson, A.E., Omordia, N., & Ugochukwu, O.E. (2010). High Lassa Fever Activity in Northern Part of Edo State, Nigeria: Re-Analysis of Confirmatory Test Results.Journal of Emergency Infectious Diseases, 17, 1698– 1700. Govinda, S.R., Eswara, R.D., (2013).Siva Prasad Diagnosis Rule-Based Expert Systems. Internal Journal of Engineering Research & Technology, 2(8), 551-561. Hambali, M.A., Akinyemi, A.A, & Luka, J.D.(2017). Expert System for Lassa Fever Diagnosis Using Rule Based Approach. Anale. Seria Informatic?.8(2), 254-259. Holland, J.J., & Domingo, E. (1997). RNA Virus Mutations and Fitness for Survival. Annual Review Microbiology. 51, 151-78 Igodan, C., Akinyokun, O., &Olatubosun, O. (2013). Online Fuzzy-Logic Knowledge Warehousing and Mining Model for The Diagnosis and Therapy of HIV/AIDS. International Journal of Computational Science and Information Technology(IJCSITY), 1(3), 27-41. Ijeoma, E.C., Ajebogah C., Oladele, O.A., Ahmed L.A., &Adesu L.A., (2021). Lassa Fever Outcomes and Prognostic Factors in Nigeria(LASCOPE): A Prospective Cohort Study. The Lancet Journal of Global Health. 9(4), 469-478. Imanov, E.,&Asengi, F.J. (2020). Artificial Intelligence for Lassa Fever Diagnosis System. InAliev, R.A., Kacprzyk, J., Pedrycz, W., Jamshidi, M., Babanli, M., &Sadikoglu, F.M. (2020): 14th International Conference on Theory and Application of Fuzzy Systems and Soft Computing. Springer Nature,pp: 540-547. Lecompte, E., Fichet-Calvin, E., Daffis, S., Koulemou, K., & Sylla, O. (2006). MastomysNatalensis and Lassa fever, West Africa. Journal of Emergency Infectious Diseases,12, 1971-1974. Marsland, S. (2015). Machine Learning: An Algorithmic Perspective. Florida, USA: CRC press. Mohssen, M., Muhammad, B. K., & Eihab B.(2017). Machine Learning Algorithms and Applications. London: CRC Press. Nnebe, S.E., Okoh, M.,Otumu, J.M.,&Oshoiribhor, E.O. (2019). A Neuro-Fuzzy Case-Based Reasoning Framework for Detecting Lassa Fever Based on Observed Symptoms. American Journal of Artificial Intelligence (AJAI), 3(1), 9-16. Olubi, O.,Oniya, E., &Owolabi, T.(2021). Development of Predictive Model for Radon Estimation in The Atmosphere Using Stepwise Regression and Grid Search Based- Random Forest Regression.Journal of the Nigerian Society of Physical Sciences, 2, 132- Oguntimilehin, A., Abiola, O.B., & Olatunji, K.A. (2015). Computer Aided Diagnostic Systems for Managing Typhoid Fever: A Review of Diagnosis Techniques. International Journal of Computer Applications, 2(3), 126-128. Oladipo, O., Olayinka, C.T., & Popoola, O.L. (2014). Mobile Compactable Expert System for The Treatment of Typhoid Fever in Developing Countries. International Journal of Computer Applications, 5(2), 16-19. Ogbu, O., Ajuluchukwu, E.,&Uneke, C.J. (2004). Lassa fever in West African sub-region: An Overview. Journal of Vector Borne Disease, March 2007, pp. 1–11. Olawole, A.S., &Knonyana, T., (2022). Forecasting Lassa Fever Outbreak Progression with Machine Learning. International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME). 3(2), 12-16. Olutola, A.,& Olufunke, C.O. (2020). Prediction of Mortality Rate of COVID-19 Patients using Machine Learning Techniques in Nigeria.International Journal of Computer Science and Software Engineering,9(5), 30-34. Omilabu, S.A., Badaru, S.O., Okokhere, P., Asogun, D., Drosten, C., &Emmerich, P., (2012). Lassa fever, Nigeria. Journal of Emergency Infectious Diseases. 11, 1642–1644. Osarumwense, A.S., &Eromosele, B.M. (2020).A Machine Learning Based Approach for Detecting Dengue Haemorrhagic Fever. International Journalof Academic & Applied Research, 4(5), 13-25. Osaseri, R. O., Onibere, E. A., &Usiobiafo, A. R. (2014). Fuzzy Expert Model for Diagnosis of Lassa Fever. Journal of the Nigerian Association of Mathematical Physics, 27, 533- Osaseri, R.O., &Osaseri, E.I. (2016) Soft Computing Approach for Diagnosis 6f Lassa Fever. International Journal of Engineering & Computer Science.10(11),67-69. Osigbemeh, M.S., Ogunwolu, F.O., Omoare, A.A.,&Inyiama, H.C. (2014). A linguistic fuzzy expert system for contagious diseases detection and isolation. Journal of Material Science and Technology, 2(1), 1-6. Putu, M. P., &Iketut, G. D. (2012). Fuzzy Knowledge-Based System with Uncertainty for Tropical Infectious Disease Diagnosis. International Journal of Computer Science Issues. 2(9), 157-163. Sarker, H. I. (2021). Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Computer Science. 2(3) 1–21. Samuel, E.N., Ozemoya, A.O., Adetokunbo, M.G., & Emmanuel, O.O. (2019). A Neuro-Fuzzy Case Based Reasoning Framework for Detecting Lassa fever Based on Observed Symptom. American Journal of Artificial Intelligence. 3(1), 9-16. Samuel,O.W., & Omisore,M.O. (2013). Genetic Neuro-Fuzzy System for The Diagnosis of Typhoid Fever. Conference on Medical Innovation and Computing Service, Pp. 1-13. Samson, K.T., Akingbade, T. &Orija, J., (2023). Comparative Analysis of Mortality Predictions from Lassa Fever in Nigeria: A Study Using Count Regression and Machine Learning Methods. Acadlore Transactions on AI and Machine Learning. 2(4), 204-2011. Steur, N.A.,&Mueller, C. (2019). Classification of viral hemorrhagic fever focusing Ebola and Lassa fever using neural networks. International Journal of Machine Learning & Computer, 9(3), 334-343. Sunil, V.A.(2021). Diseases Prediction Model using Machine Learning Technique. International Journal of Scientific Research in Science and Technology, 8(2), 121-123. Tunmibi, S., Adeniji, O., Aregbesola, A., &Dasylva, A. (2013). A Rule Based Expert System
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