Suicidal behaviour screening using machine learning techniques

In a fast-growing world, patients of anxiety and depression are more vulnerable to attempt an obnoxious step like suicide. Therefore periodic screening of these patients can be done for their wellbeing as well as to stop the negative flow of energy. We aimed to explore the potential of machine learning to identify and predict suicidal behaviour in patients with anxiety and stress by comparing the performance of machine learning algorithms (logistic regression, random forest, decision tree and multi-layer perceptron classifier). The analysis is performed using a python programming language for the screening of patients aiming to predict the risk of suicides. Random forest classifier outperforms with an accuracy of 95%. This current research work leverages the application of machine learning in the domain of the healthcare sector in the automated screening of patients. This artificial intelligence based solution reduces time consumption. This present kind of analysis can affect a remarkable monitoring system for healthcare departments.