A Logistic Regression Model to Predict Malaria Severity in Children

TL;DR

This study develops a logistic regression model to predict malaria severity in children using environmental and biological factors, achieving 83.3% accuracy in a Ghanaian district.

Contribution

It introduces a specific logistic regression model incorporating environmental and biological factors for malaria severity prediction.

Findings

83.3% accuracy in predicting malaria severity

Children are highly prone but severity remains low in the studied district

Emphasizes importance of representative sampling in machine learning models

Abstract

One of the main causes of death around the globe is malaria. Researchers have sought to develop predictive models for malaria outbreaks based on meteorological 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 dump, 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.