Using the Recall, Precision, and F1-score metrics, three model types are compared: naïve deep learning models, advanced deep learning models, and the proposed framework for multiclassification of Suspicious minor class in an imbalanced CTG test dataset.

<p>Using the Recall, Precision, and F1-score metrics, three model types are compared: naïve deep learning models, advanced deep learning models, and the proposed framework for multiclassification of Suspicious minor class in an imbalanced CTG test dataset.</p>

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Bibliographic Details
Main Author:	Amin Ullah (12015113) (author)
Other Authors:	Qazi Mazhar Ul Haq (21866718) (author), Zabeeh Ullah (21866721) (author), Jaroslav Frnda (14564125) (author), Muhammad Shahid Anwar (19660537) (author)
Published:	2025
Subjects:	Biotechnology Science Policy Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified uhb dataset confirms proposed framework solves proposed framework outperforms assessing fetal health improved clinical connectivity algorithms brings intelligence advanced dl technique advanced dl models iomt devices struggle advanced monitoring capabilities making capabilities clinical data artificial intelligence prenatal monitoring xlink "> wasserstein distance validation using study proposes study focuses small classes significance compared serious problem preventing complications pregnant women powerful solution powered networks medical things iomt systems information imbalance healthcare industry gan ), findings highlight f1 score efficiently managing defined networking deep learning controlling iomt based iomt based gans art solutions
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Using the Recall, Precision, and F1-score metrics, three model types are compared: naïve deep learning models, advanced deep learning models, and the proposed framework for multiclassification of Suspicious minor class in an imbalanced CTG test dataset.

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