Neuropredict: Forecasting Mental Wellness With Precision

Abstract

Mental health issues such as stress, anxiety, and depression are rapidly increasing due to modern lifestyle challenges, especially among students and working professionals. However, early detection is often difficult because of social stigma, lack of awareness, and limited access to mental health experts. This paper presents NEUROPREDICT: Forecasting Mental Wellness With Precision, an intelligent and user-friendly system that leverages Artificial Intelligence and Machine Learning techniques to assess mental wellness based on user responses and behavioral patterns. The proposed system utilizes structured questionnaire data, including standardized assessments like PHQ-9 and GAD-7, along with lifestyle indicators such as sleep patterns, emotional stability, and work pressure. Various machine learning algorithms, including Logistic Regression, Decision Tree, Support Vector Machine, and Random Forest, are applied to classify individuals into mental health categories such as Normal, Mild, Moderate, and Severe. Among these, the Random Forest model demonstrates superior accuracy and reliability. The system not only predicts mental health conditions but also provides meaningful insights and recommendations for self-care and professional consultation when required. By enabling early detection in a private and accessible manner, this approach aims to bridge the gap between individuals and mental healthcare support. The proposed solution is scalable, efficient, and suitable for integration into web or mobile applications, making mental health assessment more approachable and proactive.