A machine learning approach for predicting suicidal ideation in post stroke patients.

ABSTRACT: Currently, the identification of stroke patients with an increased suicide risk is mainly based on self-report questionnaires, and this method suffers from a lack of objectivity. This study developed and validated a suicide ideation (SI) prediction model using clinical data and identified SI predictors. Significant variables were selected through traditional statistical analysis based on retrospective data of 385 stroke patients; the data were collected from October 2012 to March 2014. The data were then applied to three boosting models (Xgboost, CatBoost, and LGBM) to identify the comparative and best performing models. Demographic variables that showed significant differences between the two groups were age, onset, type, socioeconomic, and education level. Additionally, functional variab

SUBMITTER: Song SI 

PROVIDER: S-EPMC9508242 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature