Performance metrics (AUC, CA, F1, Recall, MCC) of Boosting, Stacking and Voting Ensemble Learnings with SET, ADASYN and BS balancing methods in internal test, 2011 and 2014 datasets.

<p>Note: 95% CIs (AUC: Delong method; CA/F1/Recall/MCC: 1000 bootstrap samples). Model comparisons: one-way ANOVA with Bonferroni correction; *p < 0.05 vs. Stacking-ADASYN, #p < 0.05 vs. Voting-ADASYN. Key metrics for Boosting-ADASYN: internal test AUC = 0.814 (0.782–0.846), MCC = 0.493...

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محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Kai Wang (21246) (author)
منشور في:	2025
الموضوعات:	Sociology Developmental Biology Plant Biology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified shapley additive explanations chinese older adults body shape index abnormal body shape 766 &# 8211 early anxiety identification absi outperformed bmi 2008 &# 8211 anxiety symptoms xlink "> voting ), top predictors study provides study aimed stronger effects reported health positively associated interpretable tool internal validation feature selection external validation ensemble learning 79 %). 772 ). 37 %) 038 ),
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الوصف
الملخص:	<p>Note: 95% CIs (AUC: Delong method; CA/F1/Recall/MCC: 1000 bootstrap samples). Model comparisons: one-way ANOVA with Bonferroni correction; *p < 0.05 vs. Stacking-ADASYN, #p < 0.05 vs. Voting-ADASYN. Key metrics for Boosting-ADASYN: internal test AUC = 0.814 (0.782–0.846), MCC = 0.493 (0.451–0.535); 2011 external AUC = 0.772 (0.738–0.806), 2014 external AUC = 0.766 (0.732–0.800); CA = 0.754 (0.720–0.788) and MCC = 0.387 (0.345–0.429) for both 2011 and 2014.</p>

Performance metrics (AUC, CA, F1, Recall, MCC) of Boosting, Stacking and Voting Ensemble Learnings with SET, ADASYN and BS balancing methods in internal test, 2011 and 2014 datasets.

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