0.05). The net benefit of model was improved and cognition resulted in continuous reclassification improvement with NRI of 5.3%. Several clinical dimensions were significant predictors in the XGBoost classification algorithm. Limitations A limited sample size and failure to include sufficient suicide risk factors in the predictive model. Conclusion This study demonstrate that cognitive deficits may serve as an important risk factor to predict suicide attempts in patients with MDD. Combined with other demographic characteristics and attributes drawn from clinical questionnaires, cognitive function can improve the predictive effectiveness of the ML model. Additionally, explanatory ML models can help clinicians detect specific risk factors for each suicide attempter within MDD patients. These findings may be helpful for clinicians to detect those at high risk of suicide attempts quickly and accurately, and help them make proactive treatment decisions."/>
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Table 3 The testing result of XGBoost classification algorithm on suicide attempts among patients with MDD

From: Can cognition help predict suicide risk in patients with major depressive disorder? A machine learning study

  Sensitivity Specificity Accuracy AUC* PPV* NPV*
XGBoost-1 0.600 (0.323,0.837) 0.737 (0.488,0.909) 0.677 (0.495,0.826) 0.779 (0.627,0.934) 0.643 (0.351,0.872) 0.700 (0.457,0.881)
XGBoost-2 0.600 (0.323,0.837) 0.790 (0.544,0.940) 0.706 (0.525,0.849) 0.819 (0.675,0.964) 0.692 (0.386,0.909) 0.714 (0.478,0.887)
  1. *AUC Area under the curve, PPV Positive predictive value, NPV Negative predictive value