LEVERAGING MACHINE LEARNING FOR EARLY DETECTION AND PREDICTION OF CHOLERA OUTBREAKS IN NIGERIA: A DATA-DRIVEN APPROACH
DOI:
https://doi.org/10.60787/tnamp.v20.383Keywords:
Cholera Prediction, Machine Learning, Public Health, Nigeria, Infectious Disease Modeling.Abstract
Cholera remains a significant public health challenge in Nigeria, causing numerous fatalities annually. This study aims to develop a machine learning-based predictive model for early detection and prediction of cholera outbreaks in Nigeria. By integrating diverse datasets, including environmental, socio-economic, and health data, the model offers actionable insights to public health officials, enabling timely interventions and resource allocation. The study utilizes various machine learning algorithms to analyze historical data, with Random Forest emerging as the most effective. The model's predictions, validated against actual outbreak data, demonstrate its potential to significantly enhance outbreak preparedness and response strategies.
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