Performance metrices of the trained models across cross-validation folds (n = 10).

Performance metrices of the trained models across cross-validation folds (n = 10).

<p>Performance metrices of the trained models across cross-validation folds (n = 10).</p>

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Bibliographic Details
Main Author:	Shahrokh Mousavi (21989418) (author)
Other Authors:	Zahrasadat Jalalian (21989421) (author), Sima Afrashteh (18117729) (author), Akram Farhadi (21989424) (author), Iraj Nabipour (86904) (author), Bagher Larijani (96114) (author)
Published:	2025
Subjects:	Biotechnology Developmental Biology Cancer Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified thrombotic vascular diseases quadratic discriminant analysis performed using imputation performed hyperparameter tuning interaction effects inherent critical warning sign based predictive modeling svm ), k 752 ), svm 726 ), knn two ensemble models models &# 8217 highest auc values direct diagnostic methods congestive heart failure address model complexity knn ), linear 733 ), adaboost 697 ), mlp rose angina questionnaire develop machine learning indicate angina pectoris 697 ), machine learning angina pectoris scaling methods renal failure relevant linear models could following models adaboost ). dt ), model performance auc 0 probable angina definite angina xlink "> study demonstrated random forest pocosteo study parameter uncertainty nearest neighbors mean age logistic regression largely attributed fold cross elderly community dwelling population clinical relevance clinical data
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Summary:	<p>Performance metrices of the trained models across cross-validation folds (n = 10).</p>

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