A Comparative Analysis of Decision Tree and Support Vector Machine on Suicide Ideation Detection

Abstract

Suicide and self-harm are the most severe public health issues, especially in this modern era. On other side, the improvement of technology helps people can be easier to share their emotion in social media. This led us to think of making a Suicide Ideation Detection to prevent any potential suicide action being committed. Our aim for this research is to increase the capabilities of Decision Tree and Support Vector Machine algorithm, enhancing the performance of already existing research about suicide ideation detection with improved accuracy. By comparing DT and SVM, we hope we can develop a model that can effectively detect suicidal behavior, leading to a better prevention strategy, and minimizing the number of suicide attempts. The dataset used is from subreddit forums “SuicideWatch”, “depression”, and “teenagers”. We discovered that SVM performs better than Decision Tree with an overall score of 91,89% whereas Decision Tree with an overall score of 81,47%.