Mental Health Assessment and Suicide Prevention in Python Projects

Mental Health Assessment and Suicide Prevention in Python Projects

Abstract

Mental health issues and suicide are critical global challenges that require timely assessment and intervention. The project Mental Health Assessment and Suicide Prevention in Python Projects focuses on developing an intelligent system that monitors mental health indicators, predicts risk levels, and identifies individuals at risk of self-harm or suicidal tendencies using machine learning and data analytics. Python is chosen as the development platform for its extensive libraries in data analysis, natural language processing, and machine learning, including Pandas, NumPy, Scikit-learn, TensorFlow, Keras, NLTK, and SpaCy. The system collects data from surveys, questionnaires, social media posts, and wearable devices, analyzes textual, behavioral, and physiological patterns, and predicts mental health risk levels. By providing real-time alerts and recommendations, the system supports early intervention, suicide prevention, and personalized mental health care.

Existing System

Traditional methods for mental health assessment rely primarily on in-person clinical evaluations, structured questionnaires, and psychometric tests administered by trained professionals. While these methods are effective, they are often time-consuming, subjective, and inaccessible for remote monitoring or large populations. Existing digital solutions, such as mental health apps and online surveys, provide limited predictive intelligence and typically fail to detect high-risk situations in real-time. Current systems may analyze text or survey responses but are unable to integrate multiple data sources or provide actionable insights for immediate intervention, reducing their effectiveness in preventing suicide and providing timely care.

Proposed System

The proposed system introduces a Python-based framework for mental health assessment and suicide prevention using machine learning and natural language processing techniques. Data from questionnaires, surveys, social media activity, and wearable sensors is preprocessed, normalized, and transformed into structured features suitable for analysis. NLP techniques are applied to analyze text for sentiment, emotional cues, and behavioral patterns, while physiological and activity data are incorporated for a holistic assessment. Machine learning models, including Random Forest, Support Vector Machines (SVM), Gradient Boosting, and deep learning architectures such as LSTM and ANN, are trained to predict mental health conditions and suicide risk levels. The system generates real-time alerts and recommendations for healthcare providers, caregivers, or crisis response teams when high-risk patterns are detected. Performance is evaluated using metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. By integrating predictive analytics, continuous monitoring, and multi-source data, the system provides an effective, automated, and proactive approach to mental health assessment and suicide prevention, enabling timely interventions and potentially saving lives.