A Fusion-Based Framework For Explainable Suicide Attempt Prediction - Nordin, Noratikah (2024)
Suicide remains a major public health problem and one of the leading causes of death worldwide. Suicide prevention is needed to reduce global suicide mortality, as highlighted in the united nations third sustainable development goals (sdgs). A suicide attempt is the most complex and dynamic suicidal behaviour, which is important for suicide prevention strategies. However, decision-making in classifying individuals at higher risk of suicide attempts is subjective and uncertain. Existing studies on the framework for predictive models using data-driven and knowledge-driven approaches are insufficiently explained and unable to provide an understandable prediction of suicide attempts for suicide prevention in a systematic way. Therefore, this study presents a fusion-based framework for explainable suicide attempt prediction using explainable data-driven and knowledge-driven approaches to classify and explain individuals with suicide attempts to support decision-making by medical experts. The proposed work aims to analyse an explainable learning algorithms for predicting suicide attempts, propose an ontology model for semantically representing the classification risk of suicide attempts and propose an explanation generation algorithm by combining predictions from explainable machine learning and ontology models. An information fusion-based explanation generation method is proposed by integrating predictions to generate a prediction description to support decision-making. The fusion model shows that the proposed framework achieves 92% accuracy, 88% specificity, and 100% sensitivity.