Optimizing Cholera Outbreak Prediction in Nigeria: A Comparative Analysis of Machine Learning Models for Public Health Applications

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dc.contributor.authorAkanji D.O.
dc.contributor.authorAdamu M.O.
dc.date.accessioned2024-11-26T09:46:43Z
dc.date.available2024-11-26T09:46:43Z
dc.date.issued2024-11-25
dc.description.abstractCholera remains a critical public health concern in Nigeria, causing significant mortality and straining healthcare resources. Despite efforts to control the disease, recurrent outbreaks persist, especially in areas with inadequate sanitation and limited access to clean water. Traditional models for predicting cholera outbreaks often fall short due to their inability to capture the multifactorial nature of the disease's spread. This study aims to address this gap by employing regression machine learning (ML) models that integrate various socio-economic, environmental, and demographic factors to optimize the prediction of total cholera outbreak in Nigeria. The research utilized a comprehensive dataset, including variables such as population size, health facilities, water sources, poverty rates, financial inclusion, and literacy levels, spanning across 76 local government areas (LGAs) in 21 states of Nigeria. Six machine learning models Linear Regression, Decision Trees, Random Forests, XGBoost, LightGBM, and Neural Networks were developed and compared to identify the most effective predictive approach. Data preprocessing, feature engineering, and exploratory data analysis (EDA) were conducted to ensure data quality and model readiness. The results indicate that LightGBM outperforms other models, with the lowest Mean Absolute Error (MAE) and the highest R-squared value, explaining 69% of the variance in cholera cases. This model's ability to handle complex interactions between variables suggests its potential utility in real-time public health interventions. The study concludes that integrating advanced machine learning techniques into public health surveillance can significantly enhance cholera outbreak prediction and support proactive strategies to mitigate the disease's impact. These findings offer actionable insights for policymakers, public health authorities, and researchers aiming to improve disease surveillance and response in Nigeria and other developing regions.
dc.identifier.citationAkanji D.O. & Adamu M.O. (2024). “Optimizing Cholera Outbreak Prediction in Nigeria: A Comparative Analysis of Machine Learning Models for Public Health Applications". In Proceedings of the International Conference on Artificial Intelligence and Robotics (MIRG-ICAIR 2024), pp. 157-167, MIRG
dc.identifier.isbn978-978-771-680-9
dc.identifier.urihttps://ir.unilag.edu.ng/handle/123456789/13063
dc.language.isoen
dc.publisherMIRG
dc.relation.ispartofseriesMIRG-ICAIR 2024
dc.titleOptimizing Cholera Outbreak Prediction in Nigeria: A Comparative Analysis of Machine Learning Models for Public Health Applications
dc.typeArticle
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