An Ensemble Machine Learning Approach for Pre-IVF Prediction of Live Birth Outcomes

Document Type : Research Paper

Authors

1 Research Scholar (Part Time), Research Centre -Department of Electronics and Instrumentation Engineering, Dayananda Sagar College of Engineering, Bengaluru, Karnataka, India; Assistant Prof., Department of Electronics and Instrumentation Engineering, Dr. Ambedkar Institute of Technology, Bengaluru, Karnataka, India.

2 Associate Prof., Department of Electronics and Instrumentation Engineering, Dayananda Sagar College of Engineering, Bengaluru, Karnataka Affiliated to Visvesvaraya Technological University, Belagavi, Karnataka, India.

10.22059/jitm.2026.107166

Abstract

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.

Highlights

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Keywords

Journal of Information Technology Management
Volume 18, Issue 2
2026
Pages 28-43

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