A Logistic Regression Model to Predict Malaria Severity in Children

Mary Opokua Ansong, Asare Yaw Obeng and Samuel King Opoku

European Journal of Electrical Engineering and Computer Science, 2024, vol. 8, issue 2, 31-35

Abstract: One of the main causes of death around the globe is malaria. Researchers have sought to develop predicting models for malaria outbreaks based on metrological data, climate data and the breeding cycle of plasmodium, the causative agent of malaria. This study predicts the severity of malaria based on environmental and biological factors. A logistic regression model was developed in this study to predict the severity of malaria based on such factors as sickle cell disease, stagnant water, garbage dumps, wet lawns, and the use of treated mosquito nets with an 83.3% accuracy rate. The study was carried out in the Bosomtwe District of Ghana with 417 respondents. It was deduced that although children in the district are highly prone to malaria infection, the severity is very low. The study recommends that not just having a good sample size alone is important during machine learning model development but also having a good sample representation of the various class labels is equally important.

Keywords: Bosomtwe District; Children; Logistic Regression; Malaria Severity; Machine Learning (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:epw:ejece0:v:8:y:2024:i:2:id:19614

DOI: 10.24018/ejece.2024.8.2.614

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