Mobile-based application to predict children under five nutritional status using machine learning techniques
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Date
2025-07
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NM-AIST
Abstract
Malnutrition tends to be one of the most important reasons for child mortality in Tanzania and other developing countries, in most cases during the first five years of life. This research was conducted to develop a machine-learning model for predicting fetal nutritional status. Several machine learning techniques, such as AdaBoost, Logistic Regression, Support Vector Machine, Random Forest, Naïve Bayes, Decision Tree, K-nearest neighbor and Stochastic Gradient Descent. Because of their ability to predict more categorical data, so were used to categorize the children in the test dataset as "malnourished" or "nourished". These algorithms' prediction abilities' accuracy, sensitivity, and specificity were compared using performance measures such as accuracy, sensitivity, and specificity. Results show that Random Forest machine learning techniques outperformed other techniques with an accuracy of 98%. The study findings of this research are indicating a need for more attention on the nutritional status of expected mothers and children under five to be well administered with the government and the society at large by putting relevance to the suggestion that cooperation between government organizations, academia, and industry is necessary to provide sufficient infrastructure support for the future society.
Sustainable Development Goals
SDG-1 :No Poverty (indirect contribution)
SDG-3: Good Health and Well-being
SDG-17: Partnerships for the Goals