PREDICTIVE MODELING OF ADOLESCENT OBESITY USING DEEP LEARNING TECHNIQUES
Keywords:
Adolescent Obesity , Deep Learning , Predictive Modeling , Artificial Neural Networks (ANN) , Convolutional Neural Networks (CNN) , Long Short-Term Memory (LSTM)Abstract
The objective of this project is to identify the underlying causes of adolescent obesity and promote early intervention by employing sophisticated deep learning algorithms to forecast its progression. The global public health community is increasingly concerned with the issue of teenage obesity. This is a result of genetics, inadequate exercise, poor eating practices, and increasing mental stress. This paperrecommends the utilization of deep learning techniques, including ANN, CNN, and LSTM models, to analyze extensive health datasets that encompass demographic, behavioral, clinical, and lifestyle variables. The objective of the program is to accurately predict the prevalence of obesity among adolescents by analyzing a vast amount of intricate, high-dimensional data and dividing individuals into groups based on their likelihood of becoming overweight. The primary focus of the paperis the evaluation of effectiveness using metrics such as F1-score, recall, accuracy, and precision, as well as feature extraction, data cleansing, and model training. It is anticipated that the proposed approach will enhance the precision of predictions, facilitate the decision-making process for physicians, and facilitate the development of intelligent healthcare systems for the prevention and management of adolescent obesity.
