AI-Powered Suicide Prevention Through Behavioral Analysis

Abstract

This project aims to address the critical issue of suicide prevention by leveraging machine learning techniques for early detection of potential suicidal tendencies. Suicide is a global public health concern, and timely identification of individuals at risk can significantly contribute to intervention and support.

The project focuses on the utilization of diverse data sources, such as social media posts, online behavior, and multimedia content, to develop an intelligent algorithm capable of recognizing patterns indicative of suicidal thoughts or intentions. Natural language processing and sentiment analysis techniques are employed to scrutinize textual content for linguistic cues associated with distress. Furthermore, image and video analysis techniques, including facial expression recognition and body language analysis, contribute to a comprehensive approach in identifying visual indicators of self-harm or suicidal ideation.

The research involves the development and training of machine learning models on diverse datasets to ensure robust and accurate detection capabilities. The project emphasizes the ethical considerations surrounding privacy and responsible use of technology, aiming to strike a balance between aiding mental health professionals and respecting individual privacy.

The successful implementation of this project could lead to the creation of a valuable tool for mental health practitioners and support systems, enabling them to intervene proactively and provide timely assistance to individuals at risk of suicide.

Index terms

Suicide prevention, Machine learning techniques, Early detection, Suicidal tendencies, Global public health concern, Timely identification, Intervention and support, Diverse data sources, Social media posts, Online behavior, Multimedia content, Intelligent algorithm, Recognizing patterns, Suicidal thoughts or intentions, Natural language processing, Sentiment analysis, Linguistic cues, Distress detection, Image analysis techniques, Video analysis techniques, Facial expression recognition, Body language analysis, Visual indicators, Self-harm, Suicidal ideation, Machine learning models, Ethical considerations, Privacy concerns, Responsible technology use, Mental health professionals, Support systems, Proactive intervention, Timely assistance.