Performance Evaluation of Machine Learning Models for Lassa Fever Prediction BAKARI, Shehu

Dr. Yusuf Musa Malgwi  Mohammed; Usman  Mahmud Ahmad Bamanga

doi:10.56201/wjimt.v8.no3.2024.pg120.137

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Performance Evaluation of Machine Learning Models for Lassa Fever Prediction BAKARI, Shehu

Dr. Yusuf Musa Malgwi Mohammed, Usman Mahmud Ahmad Bamanga

Abstract

Lassa fever is a severe viral hemorrhagic illness endemic in parts of West Africa, primarily transmitted through contact with infected rodent excreta. Early detection and accurate diagnosis are critical to reducing mortality and controlling outbreaks. In recent years, machine learning (ML) has shown great potential in enhancing disease prediction and diagnostic accuracy. This study evaluates the performance of various ML models, including Logistic Regression, K-Nearest Neighbors, Support Vector Machine, and Naïve Bayes, in predicting Lassa fever infection. The models were trained and tested on a dataset comprising clinical and demographic features of patients. Key evaluation metrics such as accuracy, precision, recall (sensitivity), F1-score, macro- average, and weighted-average were employed to assess model performance. The Support Vector Machine (SVM) model outperformed others with an accuracy of 90%, precision of 91%, recall of 96%, and an F1-score of 93%. The findings underscore the effectiveness of SVM in developing a diagnostic model for Lassa fever, providing a foundation for deploying AI-driven diagnostic tools in resource-limited settings. Future research should explore integrating more diverse datasets and incorporating additional clinical parameters to enhance prediction accuracy further. .

Keywords

Lassa FeverMachine LearningSupport Vector Machine (SVMDiagnostic Accuracy

References

Alves, S., Lima, L., & Duarte, N. (2021). Machine learning models for diabetes prediction using clinical data. Journal of Healthcare Engineering, 2021, 6717452. Bausch, D. G., & Rollin, P. E. (2007). Diagnosis and management of Lassa fever: Current perspectives. Infectious Disease Clinics of North America, 21(4), 843-859. Choi, E., Schuetz, A., Stewart, W. F., & Sun, J. (2019). Using recurrent neural network models for early detection of sepsis. Computational Biology and Chemistry, 80, 43-49. Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118. García, V., Sánchez, J. S., & Romero, J. A. (2020). Predicting heart disease using machine learning algorithms. Journal of Biomedical Engineering and Medical Devices, 11(4), 205-214. Khatri, P., Gupta, S., & Venkatesh, S. (2020). Application of machine learning techniques for tuberculosis detection using chest X-ray images. Health Information Science and Systems, 8(1), 22. Kim, Y., Park, H., & Kim, H. (2019). Stroke risk prediction using machine learning techniques. Computers in Biology and Medicine, 113, 103388. Liu, F., Zhang, Q., Huang, Y., & Xie, J. (2021). Deep learning for COVID-19 diagnosis and prediction. Medical Image Analysis, 73, 102117. Miller, J., Wang, T., & Le, H. (2020). Predicting malaria infection using machine learning techniques. Journal of Biomedical Informatics, 107, 103496. Mohammed, U., Ibrahim, M., & Ahmed, S. (2022). Machine learning for predicting Lassa fever using symptom-based data. Journal of Infectious Diseases and Epidemiology, 13(1), 50-59. Ranjan, R., Singh, K., & Rao, S. (2021). Predicting dengue fever outbreaks using machine learning. Epidemiology and Infection, 149, e120. Rajkomar, A., Oren, E., Chen, K., & Dai, A. M. (2019). Scalable and accurate deep learning for electronic health records. npj Digital Medicine, 2(1), 1-10. Richmond, J. K., & Baglole, D. J. (2003). Lassa fever: Epidemiology, clinical features, and social consequences. BMJ, 327(7426), 1271-1275. Said, M. A., To, S. M., & Hassan, R. (2020). Current diagnostics of Lassa fever and emerging trends: A review. Journal of Infection and Public Health, 13(6), 910-917. Siddiqui, M. F., Rahman, M. S., & Chakraborty, T. (2018). Machine learning approaches for the diagnosis of typhoid fever. International Journal of Computer Applications, 180(23), 15-23. Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56. World Health Organization. (2024). Lassa fever: Fact sheet. Retrieved from https://www.who.int/news-room/fact-sheets/detail/lassa-fever.

Performance Evaluation of Machine Learning Models for Lassa Fever Prediction BAKARI, Shehu

Abstract

Keywords

References

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