The Analysis of the Risk Management in Child Obesity Using Deep Learning Neural Network

The research proposal is to manage and monitor the obesity in children by the huge database. The data base meticulously analyses the data and interpret it and derive complete details about the growth process in children by using the neural network model that identifies the obesity risk. The model then dutifully alerts parents when there is an observable increase in the child's weight, advising them proactively on what measures need to be taken to address and mitigate this weight gain. In addition to tracking the child's physical activity, the device provides parents with graphs and reports that show how the child has been performing.

The study aims at developing a neural network weights model to recognize the risk of obesity by considering the parameters like Body mass index, physical fitness level, normal heart beat rate, jumping points. The model will be trained and tested on a dataset of medical data. The performance of the model will be assessed in terms of accuracy, precision, and recall. The model will then be used to classify people into risk categories. The model will be trained using supervised learning techniques, with the relevant parameters as input and the obesity risk as the output. The model will then be tested on the dataset to evaluate the accuracy, precision, and recall. Finally, the model will be used to classify people into risk categories.