Machine Learning based Predictive Modelling for Disease Outbreak Threshold Estimation
DOI:
https://doi.org/10.48047/3rtw0c08Keywords:
Keywords: Malaria diagnosis, Blood smear image analysis, Predictive modelling, Machine learning, Multilayer perceptron.Abstract
Malaria diagnosis relied heavily on manual microscopy, where a skilled technician examines blood smears under a microscope to identify and count malaria parasites. Traditional manual methods of malaria outbreak detection rely heavily on clinical reporting, manual surveillance, and reactive interventions after the spread has already begun. These systems are often delayed, data-deficient, and incapable of providing timely warnings. Such limitations result in increased transmission rates, delayed resource mobilization, and poor risk management. The objective of this work is to leverage machine learning models to develop a robust and proactive malaria outbreak prediction system based on mosquito species data and environmental features. The motivation stems from the need for faster, more accurate, and data-driven decision-making tools to predict outbreaks before they escalate. By automating the analysis using models like Decision
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