A machine learning approach for analyzing and predicting suicidal thoughts and behaviors

Abstract

In the field of public health, suicide is a problem of the utmost significance that demands immediate attention and successful preventative measures. There has been an increase in interest in using machine learning to predict and identify people who are at a high risk of suicide as society struggles with the tremendous effects suicide has on individuals, families, and communities. In this work, we provide a complete evaluation of the state-of-the-art machine learning algorithms for suicide prediction, with the goal of highlighting the achievements made thus far and outlining potential avenues for future research. Examining the various aspects and data sources used in prior studies is essential if one wants to comprehend the complicated environment of suicide prediction. As people frequently convey their feelings, problems, and distress signals through written communication, researchers have realized the enormous utility of harnessing text-based data from social media sites. Machine learning algorithms can find patterns and signs that can point to a higher risk of suicide by examining these textual data sources. Electronic health records have also proven to be a useful tool since they include important details regarding a person's medical background, mental health diagnoses, and previous interactions with healthcare systems. The use of machine learning techniques is critical in converting a large amount of data into useful insights for suicide prevention. To evaluate the obtained data, a variety of algorithms have been used, with neural networks emerging as a major technique. Neural networks can understand complicated patterns and correlations in data, allowing them to make accurate forecasts and identify people who are suicidal. Other machine learning approaches, such as support vector machines, decision trees, and ensemble methods, have also shown promising results, demonstrating the wide range of tools available for suicide prediction. While machine learning has the potential to significantly improve suicide prevention efforts, it is critical to address the ethical considerations related to putting such models into practice. To secure individuals' sensitive information, privacy and data security problems must be properly managed. Furthermore, the potential for bias and prejudice within machine learning models must be. 5 | P a g e carefully analyzed and reduced to provide fair and equal results. Researchers and practitioners may strive toward establishing responsible and ethical suicide prediction algorithms by actively engaging with these ethical factors. This thesis focuses on the considerable advances achieved in suicide prediction via the use of machine learning techniques. Researchers have made significant progress in detecting patients at high risk of suicide by using multiple data sources such as social media, electronic health records, and demographic information, as well as employing machine learning algorithms such as neural networks. Looking ahead, machine learning has enormous potential to improve suicide prevention efforts, opening new avenues for tailored treatments and support. However, it is critical that these advances be achieved responsibly and ethically, with privacy, fairness, and equity being valued in the creation and implementation of these models.