| Sumario: | Objective: In the context of assessing suicide risk using questionnaires as measurement instruments, the main goals are: (i) to compare the performance of the classification task with knowledge-based algorithms and inferred approaches, and (ii) to reduce the set of questionnaires. Methods: A classification task is performed on the set of questionnaires considering two methods: expert knowledge translated to algorithms and represented as diagrams, and data-inferred machine learning models. Feature ablation is performed to reduce the questionnaire items. Results: Machine learning models are able to detect risk with an F1 macro average score of up to 85%, significantly better than knowledge-based models. The number of questionnaire items can be reduced with no significant impact. Conclusions: Inferred models can be used to predict the level of suicide risk with a reduced set of questionnaires. Moreover, the Suicide Cognitions Scale-Revised questionnaire is seen to be the most impactful in the prediction.
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