Univrsity Of Tehran Press Journal of Information Technology Management 2980-7972 18 2 2026 04 01 An Ensemble Machine Learning Approach for Pre-IVF Prediction of Live Birth Outcomes 28 43 107166 10.22059/jitm.2026.107166 EN Priya M K Research scholar, School of Computer Science and Engineering, REVA University, Bengaluru, Karntaka, India Lithin Kumble Associate Professor, School of Computer Science and Engineering, REVA University, Bengaluru, Karntaka, India. Journal Article 2026 05 28 This study aims to develop an AI driven model to predict in vitro fertilization (IVF) outcomes and improve the cost effectiveness of reproductive treatments. The primary objective is to estimate the likelihood of a live birth from the outset using advanced data processing techniques. Prediction models were applied to forecast live births among women undergoing their first cycle of fresh or frozen IVF or intracytoplasmic sperm injection (ICSI), incorporating both pre cycle and post cycle factors. A key focus of this work is predicting live birth probability when embryos originate from a couple rather than a donor. Using the publicly accessible Human Fertilization and Embryology Authority (HFEA) dataset, we evaluated several AI approaches, including Random Forest, Gradient Boosting, and a proposed ensemble machine learning algorithm. Data were preprocessed using the Auto Label Encoder technique. Model performance was assessed through confusion matrices, F1 scores, precision, recall, and receiver operating characteristic (ROC) curves. The ensemble algorithm achieved the strongest overall performance, with accuracy of 79, precision of 77, recall of 76, and an F1 score of 76.49. Gradient Boosting demonstrated the highest recall (80.48) but showed average performance in other metrics. Random Forest yielded comparatively lower accuracy and F1 scores, indicating limitations in class differentiation. These findings suggest that ensemble based AI models offer a more robust approach for predicting live birth outcomes in IVF treatment pathways. https://jitm.ut.ac.ir/article_107166_48fddf196b2205f8b335c0a295bdbf74.pdf