Using Machine Learning Techniques to predict malaria prevalence in Rwanda

Abstract Malaria is a terrible communicable disease that leads to the death of people every day throughout the world despite the effort made to eradicate it. This vector-borne disease affects Africa because of limited medical resource, lack of information and other socio-economic factors. In report of WHO 2019, there is an estimation of 228 million of malaria cases globally and in 2023 WHO reported the increase of malaria to 249 million. This increase is associated with many risk factors and those factors mitigated to eradicate malaria. This research aims to predict malaria prevalence in Rwanda. We have managed to reveal factors contribute to malaria outbreak like region, type of place of residence, district, and wealth index, protect against malaria by sleeping under a mosquito net, protect against malaria by sleeping under an ITN, protect against malaria by cutting grass around house, seen or heard any messages about malaria and person slept an ever-treated net. Those factors are associated with the malaria prevalence either positively or negatively. The logistic regression model is designed to provide future prediction offering valuable insights to mitigate the impact of malaria outbreaks. This approach will enable healthcare providers and authorities to intervene in advance by reducing the severity of malaria and allocate resources where will be mostly needed. This dissertation also provides explanation to the methodology used to develop predictive models where we used data from RDHS from 2019 to 2020 both trained dataset and tested dataset, the sample size was 5041 where we used 16 variables of interest.