WEIGHT CONTROL IN CHILDREN USING MACHINE LEARNING ALGORITHM EMBEDDED IN INTERNET OF THINGS

Abstract

Millions of people worldwide, particularly children, have suffered fatal consequences due to the issues of weight anomalies including overweight, obesity, stunted growth and the likes resulting from malnutrition. Early detection and treatment of these problems will help to eliminate or at least minimize the incidences of weight problems before and as well in adulthood.   This study explores the role of IoT in monitoring children's weight to mitigate weight disorders and in reducing child mortality, diseases, and other issues linked to excess weight. It also introduces the use of machine learning’ clustering technique, feature selection, classification, cross validation technique, which will be used in IoT devices to monitor, predict and treat children with obesity problems. Upon completion of implementation, it is anticipated that the suggested system will effectively monitor patients believed to have weight-related problems, forecast their health conditions, and suggest treatments with minimal input from healthcare professionals. Additionally, it is anticipated that the system will demonstrate improved performance, greater accuracy in comparison to current systems, and ensure that the model remains robust against overfitting.