Abstract:
Suicide is an unnatural death and it becomes a major issue in Bangladesh. The World health
organization report which is published in 2018, it says, Suicide Deaths in Bangladesh come
across 9,544 deaths, or in percentage, it is 1.23% of entire deaths [1]. We aimed to develop
a model that can predict suicidal thoughts, using a machine learning algorithm. It can prevent
future risk of suicidal attempts. Dataset presents 15-46 years old people's thoughts, feelings,
their regular activities, contains a total of 22 attributes and 441 instances. The classification
process was performed using nine machine learning algorithms those are, Naive Bayes,
KNN, Linear SVC (support vector classifier), Non-linear SVC, Random Forest Classifier
(RFC), Decision Tree, Logistic Regression (LR), and Extreme Gradient Boosting (XGB)
Classifier, Adaptive Boosting(Ada-boost) Classifier. The prediction model achieved a good
performance. The highest accuracy achieved Random Forest Classifier (0.91). The area
under the receiver operating characteristic curve (AUC)=0.9 for Random Forest Classifier.
This study shows the probability that a machine learning approach can able to decrease
suicide risk. Hopefully, this model will assist as a support for reducing future suicidal risk.
The paper ends with a review of various practical issues, which may be explored to enhance
model performance.
