Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Upreti, Kamal; George, Jossy; Hundekari, Sheela; Alam, Mohammad Shabbir Title A name given to the resource Optimized Hybrid Prognostics Using Hynetreg Model for Infertility Prediction Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Biomedical and Pharmacology Journal;Volume;18;Issue;2;pp.1272-1288 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.13005/bpj/3168" target="_blank" rel="noreferrer noopener">https://doi.org/10.13005/bpj/3168</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105010881684?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105010881684?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Upreti K., Department of Computer Science, CHRIST (Deemed to be University), Delhi NCR, Uttar Pradesh, Ghaziabad, India; George J., Department of Computer Science, CHRIST (Deemed to be University), Delhi NCR, Uttar Pradesh, Ghaziabad, India; Hundekari S., Department of Computer Science and Engineering, Pimpri Chinchwad University, Maval Talegaon, Maharashtra, Pune, India; Alam M.S., Department of Computer Science, College of Computer Science and Information Technology, Jazan University, Jazan, Saudi Arabia Description An account of the resource This paper develops an optimized hybrid approach to predict infertility with the HyNetReg Model. The HyNetReg Model combines deep feature extraction by using neural networks with logistic regression with regularization. It uses both hormonal and demographic information of 100 participants to clarify intricate interlinkages between demographic factors and salient hormonal levels, such as Luteinizing Hormone, Follicle Stimulating Hormone, Anti-Mlerian Hormone, and Prolactin, and the ability of these same factors to affect fertility outcomes. It applies heavy data pre-processing including normalization, missing values imputation, and class imbalance handling through oversampling techniques. A multi-layer neural network is utilized to extract features for the reduction of complex, non-linear interaction among the input variables. Then, regularized logistic regression is applied for classification on the same features. Performance evaluation metrics, including accuracy, precision, recall, F1-score, and ROC curve analysis, demonstrate the superiority of the HyNetReg Model over traditional logistic regression. The ROC curve was specifically utilized to assess the models discrimination ability between infertile and fertile cases by plotting the true positive rate (sensitivity) against the false positive rate (1-specificity). A higher Area Under the Curve indicated that the model effectively distinguished infertility risks based on hormonal and demographic features. The results indicate that the model can recover very slight interdependencies of hormones and influences of demographics, making it suitable for modeling multi-factorial determinants of infertility and holding significant implications for clinical decision-making. 2025 Oriental Scientific Publishing Company. All rights reserved. Subject The topic of the resource Alpha-Feto-Protein (AFP); Anti-Mlerian Hormone (AMH); Blood Urea Nitrogen (BUN); Follicle Stimulating Hormone (FSH); Idiopathic Female Infertility (IFI); Luteinizing Hormone (LH); multi-layer perceptron (MLP); poor ovarian response (POR); recurrent reproductive failure (RRF); total motile sperm count (TMSC) Publisher An entity responsible for making the resource available Oriental Scientific Publishing Company Relation A related resource ISSN: 9746242; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online