Malnutrition Detection Analysis and Nutritional Treatment Using Ensemble Learning

Malnutrition is a condition that arises when an individual’s diet contains excessive amounts of certain nutrients or insufficient amounts of one or more of the essential nutrients. The proposed system uses an ensemble learning model of the CNN, the transfer learning algorithms such as Inception-v3, VGG16 and VGG19 were combined together with the help of ensemble learning to enhance classification, prediction, function approximation, etc. The model takes input images and classifies them as normal, wasting, stunting, and obesity. The goal of the proposed system is to identify malnutrition and its types and provide treatments for each category and ways to prevent malnutrition which will assist in lowering the danger of mortality, health and physical problems by using the appropriate treatments or precautions. In conclusion, the proposed system is a significant step towards identifying and treating malnutrition effectively. By using ensemble learning algorithms, it can accurately classify different types of malnutrition and provide appropriate treatments to those affected.