JJEM: Special Edition 2 (December 2024)
JJEM: Special Edition 2 (December 2024)
2024-12-08
Machine learning techniques for identification of malnutrition in children.
Roopa D M, Mr. Santhosh S G
Malnutrition poses a threat to world health, particularly in underdeveloped nations where early identification is essential. In order to detect malnutrition. This research integrates Convolutional Neural Networks (CNNs) with critical parameters like age, weight, and height. Users upload images, which CNN analyses to find features that might point to a person's nutritional status. Along with age, weight, and height, these factors are applied to classify persons as either malnourished or healthy. A user must first upload an image for the system to function, after which CNN examines the image to look for obvious indicators of malnutrition. These factors are integrated with anthropometric information, and a classification algorithm is used to evaluate the nutritional status. The system aims to detect malnutrition in children, assisting individuals and healthcare providers in mitigating the impacts of malnutrition through automated implementation rather than relying on manual processes.
Malnutrition, Convolutional Neural Network, Machine Learning, Artificial Intelligence.