AI-Driven Mental Health Surveillance: Identifying Suicidal Ideation Through Machine Learning Techniques

As global suicide rates continue to rise, the demand for innovative, data-driven solutions in mental health surveillance has never been more urgent. This study harnesses the power of advanced artificial intelligence (AI) and machine learning techniques to detect suicidal ideation from Twitter data, presenting a groundbreaking approach to suicide prevention. A robust, real-time predictive model was developed to process vast volumes of social media posts, integrating natural language processing (NLP) and sentiment analysis to identify textual and emotional cues indicative of distress. This approach enables precise detection of potential suicide risks while significantly minimizing false positives, paving the way for more accurate and effective mental health interventions. The study's findings highlight the transformative potential of machine learning in suicide prevention. By uncovering behavioral patterns and context-specific triggers such as social isolation and bullying, it establishes a benchmark for the application of AI in sensitive, real-time mental health contexts. The proposed framework offers a scalable, high-performance tool for timely, data-driven responses, contributing substantially to global suicide prevention strategies. The model demonstrated exceptional predictive performance, achieving an overall accuracy of 85%, a precision of 88%, and a recall of 83% in detecting "Potential Suicide Posts." High-quality data transformation was ensured through advanced preprocessing techniques, including tokenization, stemming, and feature extraction using Term Frequency-Inverse Document Frequency (TF-IDF) and Count Vectorization. A Random Forest Classifier, chosen for its robustness in handling high-dimensional data, effectively captured linguistic and emotional patterns associated with suicidal ideation. The model’s reliability was further validated with an impressive Precision-Recall AUC score of 0.93, solidifying its efficacy as a powerful tool for real-time mental health surveillance and intervention.