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 Jain, Rituraj; Shankar, Uma; Radhakrishnan, G V; Date, Saroj; Upreti, Kamal; Caroline, S.; Babu, P Ramesh; Dayal, Mohit Title A name given to the resource Deep and Hybrid Ensemble Learning Methods for Enhanced Live-Birth Prediction in Fertility Treatments 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 Informatica (Slovenia);Volume;49;Issue;7;pp.407-422 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.31449/INF.V46I7.8521" target="_blank" rel="noreferrer noopener">https://doi.org/10.31449/INF.V46I7.8521</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105027330097?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105027330097?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 Jain R., Department of Information Technology, Marwadi University, Gujarat, Rajkot, India; Shankar U., Faculty of Management and Social Sciences, Qaiwan International University, Kurdistan, Sulaimanyah, Iraq; Radhakrishnan G.V., Kalinga School of Management, Kalinga Institute of Industrial Technology, Bhubaneswar, India; Date S., Department of Artificial Intelligence and Data Science, CSMSS Chh. Shahu college of Engineering, Chh. Sambhajinagar, Maharashtra, India; Upreti K., Department of Computer Science, Christ University, Delhi NCR Campus, Ghaziabad, India; Caroline S., Department of Electronics and Communication Engineering, St. Xavier's Catholic College of Engineering, Tamil Nadu, Nagercoil, India; Babu P.R., Department of Computer Science &amp; Engineering, Narsimha Reddy Engineering College, Telangana, Secunderabad, India; Dayal M., Department of Information Technology, Bharati Vidyapeeth's College of Engineering, New Delhi, India Description An account of the resource The prediction of live birth outcomes using Assisted Reproductive Technologies (ART) remains a complex task owing to the high inter-patient variability and non-linear clinical interactions. This study presents a comparative evaluation of hybrid machine-learning models to improve in vitro fertilization (IVF) success prediction using a real-world anonymized dataset of 2,000 ART cases. After pre-processing (including missing value imputation, feature selection via Recursive Feature Elimination with Cross-Validation, and class balancing using SMOTE with k=5), four hybrid models were developed: stacking with XGBoost as the meta-learner, weighted ensemble, autoencoder-based feature fusion, and cascading classifiers. Models were evaluated using accuracy, AUC, precision, recall, and F1-score metrics, and compared against a baseline Random Forest classifier. The stacking model (XGBoost with Random Forest, MLP, and SVM base learners) achieved the best performance, with an accuracy and 0.999 AUC of 0.985. The weighted hybrid ensemble followed an accuracy of 0.953 and AUC of 0.994. The statistical significance of the improvements was confirmed using Wilcoxon Signed-Rank and McNemars tests (p &lt; 0.05). To enhance model transparency, SHapley Additive exPlanations (SHAP) was applied to interpret base model contributions in the stacking architecture. These results support the application of AI-driven hybrid modelling for personalized IVF treatment planning. Future work will focus on prospective validation and clinical decision support system (CDSS) integration to assess deployment feasibility. (2025), (Slovene Society Informatika). All rights reserved. Subject The topic of the resource assisted reproductive technologies; hybrid machine learning; IVF prediction; live-birth estimation; predictive modelling Publisher An entity responsible for making the resource available Slovene Society Informatika Relation A related resource ISSN: 3505596; CODEN: INFOF 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 Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online