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Machine learning (ML) and artificial intelligence case study supports to increase the accuracy level of prediction and aid the goal of suicide prevention. This paper reviews literature concerning the machine learning methods used to help identify various risk factors and help prevent suicide. This existing method our research and analysis finding wrong prediction which were used to identify various suicide risk factors and additional analysis of whether there are any correlations or variations in the risk factors from pre- and post-pandemic datasets regarding suicide rates low. The proposed method using to human behavior based predict the suicide implementation of Recurrent Neural Network algorithm (RNN) using human behavior data sets and preprocess, classified of the suicide or non-suicide prevention. The dataset obtained from suggest that high levels of risk factor identification are possible and this study and the analysis serve as supporting research and monitor to relief in the continued ambitious goal of suicide prevention. As a result, the focus of this study is to illustrate some of the computational strategies utilized in the framework proposed in this study can increase the accuracy machine learning to predicting at risk of suicide prevention.

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How to Cite
Parama Guru. S, & B.Narmada. (2023). Machine learning based suicide prediction using RNN. International Journal of Intellectual Advancements and Research in Engineering Computations, 11(1), 36–41. Retrieved from