PREDICTION OF MALNUTRITION IN CHILDREN USING MACHINE LEARNING

Abstract: Malnutrition is an imbalance between the nutrients the human body wants and the nutrients it receives. Infant malnutrition is a grave health issue for every country. Children are the fateful framework of a country and as a result this issue affects monetary boom and rural development without delay. Anthropometric measurements such as WAZ, HAZ, WHZ, BMI, MUAC are used to assess an individual's dietary reputation. In this paper, we have designed an automation machine for predicting malnutrition in under five children and recommending a dietary regimen for predicted malnutrition. The layout of this venture is primarily based on a theoretical framework that has been developed utilizing the accumulated literature. In conclusion, by determining the malnutrition status and administering the diet regimen policymakers can reduce malnutrition condition. The "Prediction of Malnutrition in Children" project is dedicated to creating a reliable model for early detection and forecasting of malnutrition among children. Through meticulous data analysis and the application of advanced machine learning techniques, the project aims to accurately predict malnutrition risk by integrating diverse datasets covering nutritional, demographic, medical, and socio-economic factors. By doing so, it seeks to revolutionize intervention strategies, enabling timely and targeted interventions to mitigate the impact of malnutrition on children's health. Ethical considerations, data privacy, and stakeholder engagement are central to its approach, ensuring transparency, accountability, and respect for privacy rights. Through interdisciplinary collaboration and community involvement, the project aims to drive innovation and contribute to the global effort to combat malnutrition, ultimately promoting a healthier future for children worldwide.