Smart Mental Health Monitoring System using Wellness Score Analytics

This paper presents an intelligent and data-driven system for the Analysis of Depression Risk using
advanced Machine Learning techniques. Depression is one of the most common and serious mental health
disorders affecting people across different age groups, particularly students and working professionals
who often face academic pressure, career uncertainty, workload stress, and social challenges. In today’s
fast-paced environment, mental health issues frequently go unnoticed until they become severe. Therefore,
early identification of depression risk plays a crucial role in providing timely emotional support,
counselling, and preventive care before the condition worsens. The proposed system focuses on analysing
multiple behavioural, lifestyle, and psychological factors that are commonly associated with depression.
These factors include sleep patterns, academic pressure, work-related stress, level of social interaction,
daily habits, and emotional state. By collecting and examining these attributes, the system aims to detect
patterns that may indicate a higher likelihood of depression. Instead of relying solely on subjective
observation, this approach uses data-driven insights to support more objective and consistent evaluation.
Unlike traditional psychological assessments, which often require manual evaluation by mental health
professionals through interviews and questionnaires, the proposed system automates the analysis process
using supervised machine learning models. The system accepts user-provided inputs, performs data
preprocessing such as cleaning, encoding, and normalization, and then applies trained classification
models to estimate the individual’s depression risk level. Various machine learning algorithms are evaluated to determine the most accurate and reliable model for prediction. Experimental results demonstrate promising levels of accuracy, precision, and reliability in identifying individuals who may fall into moderate or high-risk categories. The system is designed not as a replacement for professional diagnosis, but as a supportive and preliminary screening tool that can assist in early detection and
awareness.