Transparent Peer Review By Scholar9
An Application for the Earlier Detection of Suicide with Machine Learning Techniques.
Abstract
In 2023, Tamil Nadu ranked as the second-highest state in reported suicides in India, accounting for 18,925 cases, according to the National Crime Records Bureau (NCRB). This alarming statistic highlights the pervasive impact of suicide on individuals, families, and communities. Investigations revealed depression and anxiety as significant risk factors, particularly among students facing intense academic pressures. India's meritorious pastoral educational institutions reported 49 suicides in a short period, with half of the victims belonging to marginalized communities, attributed to mounting academic demands. Research findings indicate a substantial increase in the risk of self-harm (84.5%) associated with the confluence of depression and anxiety. Digital Behaviour (DB) analysis of digital natives reveals a correlation between depressive symptoms and increased email usage, along with high Flow Duration Entropy (FDE) in Internet activities as markers of depression and anxiety. FDE, measuring fluctuations in digital application usage, becomes crucial in understanding disorder and predictability in flow data. Prolonged screen time is longitudinally associated with higher anxiety and depression symptoms, emphasizing its significance in adolescents. This data underscores the urgent need to address mental health concerns and implement proactive measures. This project proposes a software application with Machine Learning techniques. The application aims to comprehensively monitor depression, anxiety, and the duration of suicide ideation probabilities. Upon detection, students will receive tailored monitoring and counselling promptly, with the primary objective of identifying and addressing potential mental health concerns for suicide prevention. The implementation of this monitoring and support system seeks to promote the overall well-being of students and cultivate a safe educational environment, with potential applications across various sectors of society.
Murali Mohana Krishna Dandu Reviewer
16 Sep 2024 03:10 PM
Approved
Relevance and Originality
The report addresses a critical issue—the high suicide rates in Tamil Nadu, particularly among students under academic stress. By focusing on the correlation between digital behavior and mental health, and proposing a machine learning-based monitoring application, the study is highly relevant. The originality of the research lies in its integration of digital behavior analysis with mental health monitoring, aiming to provide timely intervention and support for suicide prevention.
Methodology
The study employs a combination of digital behavior analysis and machine learning techniques to monitor mental health indicators. By analyzing email usage and Flow Duration Entropy (FDE) in Internet activities, the methodology offers a novel approach to identifying depression and anxiety. However, the abstract lacks details on how data were collected and how the machine learning model was developed and validated, which are crucial for understanding the methodology’s effectiveness.
Validity & Reliability
The research suggests a strong approach to validity and reliability by using digital behavior metrics and machine learning for mental health monitoring. However, the abstract does not specify how the validity of these metrics and the reliability of the machine learning model were assessed. Additional details on how the data's reliability was ensured and the model's performance metrics would enhance the credibility of the findings.
Clarity and Structure
The report is generally clear and well-structured, addressing the impact of digital behavior on mental health and proposing a software solution. It effectively highlights the urgent need for mental health support and the potential benefits of the proposed application. However, more detailed explanations of how the software application functions and integrates machine learning would improve clarity and provide a better understanding of the proposed solution’s implementation.
Result Analysis
The report indicates a significant correlation between digital behavior patterns and mental health issues, with potential for using machine learning to detect and address these concerns. While it suggests that the proposed application could improve student well-being and prevent suicides, the abstract lacks specific data or results from preliminary tests of the application. Detailed analysis of the application’s performance and its impact on mental health would offer a clearer picture of its effectiveness in real-world settings.
4o mini
IJ Publication Publisher
Done Sir
Murali Mohana Krishna Dandu Reviewer