@Article{iasc.2025.061622,
AUTHOR = {Ali Hasan Dakheel, Mohammed Raheem Mohammed, Zainab Ali Abd Alhuseen, Wassan Adnan Hashim},
TITLE = {Deep Learning-Based Decision Support System for Predicting Pregnancy Risk Levels through Cardiotocograph (CTG) Imaging Analysis},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {40},
YEAR = {2025},
NUMBER = {1},
PAGES = {195--220},
URL = {http://www.techscience.com/iasc/v40n1/59693},
ISSN = {2326-005X},
ABSTRACT = {The prediction of pregnancy-related hazards must be accurate and timely to safeguard mother and fetal health. This study aims to enhance risk prediction in pregnancy with a novel deep learning model based on a Long Short-Term Memory (LSTM) generator, designed to capture temporal relationships in cardiotocography (CTG) data. This methodology integrates CTG signals with demographic characteristics and utilizes preprocessing techniques such as noise reduction, normalization, and segmentation to create high-quality input for the model. It uses convolutional layers to extract spatial information, followed by LSTM layers to model sequences for superior predictive performance. The overall results show that the model is robust, with an accuracy of 91.5%, precision of 89.8%, recall of 90.4%, and F1-score of 90.1% that outperformed the corresponding baseline models, CNN (Convolutional Neural Network) and traditional RNN (Recurrent Neural Network), by 2.3% and 6.1%, respectively. Rather, the ability to detect pregnancy-related abnormalities has considerable therapeutic potential, with the possibility for focused treatments and individualized maternal healthcare approaches, the research team concluded.},
DOI = {10.32604/iasc.2025.061622}
}