Machine Learning Framework for Prediction of Early Childhood Obesity: A Case of Zimbabwe

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Author(s): Panashe Chiurunge (Graduate Business School, Chinhoyi University of Technology, Zimbabwe)and Agripah Kandiero (Insituto Superior Mutasa (ISMU), Mozambique & Mozambique Institute of Technology, Mozambique & Africa University, Zimbabwe)
Copyright: 2023
Pages: 29
Source title: Integrating Digital Health Strategies for Effective Administration
Source Author(s)/Editor(s): Ahmed Chemseddine Bouarar (University of Medea, Algeria), Kamel Mouloudj (University of Medea, Algeria)and Dachel Martínez Asanza (University of Medical Sciences of Havana, Cuba)
DOI: 10.4018/978-1-6684-8337-4.ch014

Keywords: Business Science Reference / Health Information Systems / Healthcare Administration / Medicine & Healthcare

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Abstract

One of the challenges being faced in the 21st century is child obesity, which is a serious health concern in Zimbabwe and the world. If obesity is not controlled, it has detrimental consequences when a child risks suffering from health challenges such as cancer, type 2 diabetes, heart disease, and osteoarthritis in adulthood. Therefore, it is paramount to have an early prediction of child obesity using the BMI scale. Technology has the unrealized potential to identify people at risk for behavioural health conditions and inform prevention and intervention strategies. In this study, a prediction model was proposed to investigate how technology can be used to predict child obesity. Using a prediction model, this study sought to understand technology's potential value in child obesity. Three different machine learning methods were used to establish accuracy in the prediction model. The findings of this study indicate that it is feasible to use a prediction tool to identify individuals at risk of being diagnosed with obesity, which can facilitate early intervention and improved outcomes.

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Machine Learning Framework for Prediction of Early Childhood Obesity: A Case of Zimbabwe

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