The Study of Significance of Using Machine Learning in Detection of Suicidal Tendencies/Depression in Students and Teenagers During a Social Media Post & Video Call

Abstract

The provided text discusses the application of machine learning techniques for suicide detection and prevention. Several approaches are explored, including:
● Direct Detection: Using machine learning to analyze text from social media posts and other online sources to identify individuals who may be at risk of suicide.
● Indirect Detection: Utilizing computer vision techniques to detect potential suicide attempts, such as depressed face or even detecting hanging, through video surveillance. This will be covered in the future scope of this research.
The limitations and challenges of these methods are also highlighted, including data quality, model accuracy, and ethical considerations.
Key findings and future directions:
● Machine learning shows promise: It can effectively detect suicide ideation and potential attempts.
● Data quality is crucial: High-quality, annotated data is essential for training accurate models.
● Human intervention is necessary: While machine learning can provide valuable insights, human judgment is still needed to make informed decisions.
Future research: Focus on improving model accuracy, addressing ethical concerns, and exploring new techniques like deep learning and natural language processing. Utilize the RestAPI end points during video calls can play a crucial role in detecting any negative thoughts. Introducing other vital metrics such as BMI, Blood pressure etc. may further enhance the accuracy of our model’s findings.
Overall, the text emphasizes the potential of machine learning to revolutionize suicide prevention but acknowledges the need for continued research and development.