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

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

Panashe Chiurunge, Agripah Kandiero
Copyright: © 2023 |Pages: 29
ISBN13: 9781668483374|ISBN10: 1668483378|EISBN13: 9781668483381
DOI: 10.4018/978-1-6684-8337-4.ch014
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MLA

Chiurunge, Panashe, and Agripah Kandiero. "Machine Learning Framework for Prediction of Early Childhood Obesity: A Case of Zimbabwe." Integrating Digital Health Strategies for Effective Administration, edited by Ahmed Chemseddine Bouarar, et al., IGI Global, 2023, pp. 281-309. https://doi.org/10.4018/978-1-6684-8337-4.ch014

APA

Chiurunge, P. & Kandiero, A. (2023). Machine Learning Framework for Prediction of Early Childhood Obesity: A Case of Zimbabwe. In A. Bouarar, K. Mouloudj, & D. Martínez Asanza (Eds.), Integrating Digital Health Strategies for Effective Administration (pp. 281-309). IGI Global. https://doi.org/10.4018/978-1-6684-8337-4.ch014

Chicago

Chiurunge, Panashe, and Agripah Kandiero. "Machine Learning Framework for Prediction of Early Childhood Obesity: A Case of Zimbabwe." In Integrating Digital Health Strategies for Effective Administration, edited by Ahmed Chemseddine Bouarar, Kamel Mouloudj, and Dachel Martínez Asanza, 281-309. Hershey, PA: IGI Global, 2023. https://doi.org/10.4018/978-1-6684-8337-4.ch014

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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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