Study on Mental Health and Obesity Among Children Based on Daily Activities and Effects Using Machine Learning

The goal of this research is to comprehend correlation between the daily activities and mental health of the children which have been under discussion over the recent years. Children (age 12 to 17) who suffer due to mental health issues are often stressed and depressed and as a result are said to fall into unhealthy eating habits which in turn causes obesity and various other heart diseases. Thus, in this study we will be identifying the daily activities that has major contributions towards the condition of becoming overweight, obese and mentally ill by using historical and statistical data along with ensemble machine learning techniques. The extra trees algorithm will be used to find the important features and their scores in this study. Further, In the end we will draw conclusions by distinguishing the features based on their scores and by giving them labels in order to observe an insight on major and minor contributions towards conditions for children on a day-to-day basis. The findings of this study can also inform future research on the complex relationship between daily activities, mental health, and obesity. Ultimately, the study aims to shed light on the factors that contribute to the health and well-being of children, which can have significant long-term implications for their physical and mental health as they transition into adulthood.