Developing Solutions for Assessing Mental Health and Well-Being in Youth

Abstract

Mental health issues among youth are a growing concern, significantly impacting their academic performance, social relationships, and overall well-being. Many young individuals face barriers for seeking help due to stigma, limited resources, and inadequate awareness, which can worsen their mental health challenges. Mental health during the early life can influence overall development and well-being.This paper presents a machine learning-based mental health and well-being surveillance assessment specifically designed for youth. The system employs a Random Forest Classifier to analyze structured user input data collected through a comprehensive questionnaire, enabling accurate predictions of mental health conditions such as anxiety and depression, ptsd, stress. By providing personalized recommendations and resources tailored to the unique needs of young individuals, the solution fosters a safe and stigma-free environment that encourages proactive engagement with mental health. The implementation includes a user feedback mechanism that allows for continuous refinement of the machine learning model, enhancing the effectiveness of the recommendations over time.