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M K, Priya, Kumble, Lithin. (1405). An Ensemble Machine Learning Approach for Pre-IVF Prediction of Live Birth Outcomes. , 18(2), 28-43. doi: 10.22059/jitm.2026.107166
Priya M K; Lithin Kumble. "An Ensemble Machine Learning Approach for Pre-IVF Prediction of Live Birth Outcomes". , 18, 2, 1405, 28-43. doi: 10.22059/jitm.2026.107166
M K, Priya, Kumble, Lithin. (1405). 'An Ensemble Machine Learning Approach for Pre-IVF Prediction of Live Birth Outcomes', , 18(2), pp. 28-43. doi: 10.22059/jitm.2026.107166
M K, Priya, Kumble, Lithin. An Ensemble Machine Learning Approach for Pre-IVF Prediction of Live Birth Outcomes. , 1405; 18(2): 28-43. doi: 10.22059/jitm.2026.107166

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

Journal of Information Technology Management
دوره 18، شماره 2، 2026، صفحه 28-43    اصل مقاله (1.22 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/jitm.2026.107166
نویسندگان
Priya M K* 1؛ Lithin Kumble2
1Research scholar, School of Computer Science and Engineering, REVA University, Bengaluru, Karntaka, India
2Associate Professor, School of Computer Science and Engineering, REVA University, Bengaluru, Karntaka, India.
چکیده
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.

تازه های تحقیق

Capuzzi, E., Caldiroli, A., Ciscato, V., Zanvit, F. G., Bollati, V., Barkin, J. L. & Buoli, M. (2020). Is in vitro fertilization (IVF) associated with perinatal affective disorders?. Journal of affective disorders, 277, 271-278.

Gui, J., Ling, Z., Hou, X., Fan, Y., Xie, K., & Shen, R. (2020). In vitro fertilization is associated with the onset and progression of preeclampsia. Placenta, 89, 50-57.

Huang, B., Zheng, S., Ma, B., Yang, Y., Zhang, S., & Jin, L. (2022). Using deep learning to predict the outcome of live birth from more than 10,000 embryo data. BMC pregnancy and childbirth, 22(1), 36.

Koumparou, M., Bakas, P., Pantos, K., Economou, M., & Chrousos, G. (2021). Stress management and In Vitro Fertilization (IVF): A pilot randomized controlled trial. Psychiatriki, 32(4), 290-9.

Lafontaine, S., Labrecque, R., Blondin, P., Cue, R. I., & Sirard, M. A. (2023). Comparison of cattle derived from in vitro fertilization, multiple ovulation embryo transfer, and artificial insemination for milk production and fertility traits. Journal of dairy science, 106(6), 4380-4396.

Louis, C. M., Erwin, A., Handayani, N., Polim, A. A., Boediono, A., & Sini, I. (2021). Review of computer vision application in in vitro fertilization: the application of deep learning-based computer vision technology in the world of IVF. Journal of assisted reproduction and genetics, 38(7), 1627-1639.

Luke, B., Brown, M. B., Eisenberg, M. L., Callan, C., Botting, B. J., Pacey, A. & Baker, V. L. (2020). In vitro fertilization and risk for hypertensive disorders of pregnancy: associations with treatment parameters. American journal of obstetrics and gynecology, 222(4), 350-e1.

Nagy, Z. P., Shapiro, D., & Chang, C. C. (2020). Vitrification of the human embryo: a more efficient and safer in vitro fertilization treatment. Fertility and sterility, 113(2), 241-247.

Peipert, B. J., Chung, E. H., Harris, B. S., Warren, C. M., & Jain, T. (2022). Impact of comprehensive state insurance mandates on in vitro fertilization utilization, embryo transfer practices, and outcomes in the United States. American Journal of Obstetrics and Gynecology, 227(1), 64-e1.

Peng, J., Geng, X., Zhao, Y., Hou, Z., Tian, X., Liu, X. & Liu, Y. (2024). Machine learning algorithms in constructing prediction models for assisted reproductive technology (ART) related live birth outcomes. Scientific Reports, 14(1), 32083.

Pinborg, A., Wennerholm, U. B., Romundstad, L. B., Loft, A., Aittomaki, K., Söderström-Anttila, V. & Bergh, C. J. H. R. U. (2013). Why do singletons conceived after assisted reproduction technology have adverse perinatal outcome? Systematic review and meta-analysis. Human reproduction update, 19(2), 87-104.

Salmanian, B, et al. In vitro fertilization as an independent risk factor for placenta accreta spectrum. American journal of obstetrics and gynecology 223.4 (2020): 568-e1.

Sanderman, E. A., Willis, S. K., & Wise, L. A. (2022). Female dietary patterns and outcomes of in vitro fertilization (IVF): a systematic literature review. Nutrition journal, 21(1), 5.

Shingshetty, L., Cameron, N. J., Mclernon, D. J., & Bhattacharya, S. (2024). Predictors of success after in vitro fertilization. Fertility and Sterility, 121(5), 742-751.

Steptoe, P. C., & Edwards, R. G. (1978). Birth after the reimplantation of a human embryo.

Tiitinen, A. (2019). Single embryo transfer: why and how to identify the embryo with the best developmental potential. Best Practice & Research Clinical Endocrinology & Metabolism, 33(1), 77-88.

Von Schondorf-Gleicher, A., Mochizuki, L., Orvieto, R., Patrizio, P., Caplan, A. S., & Gleicher, N. (2022). Revisiting selected ethical aspects of current clinical in vitro fertilization (IVF) practice. Journal of assisted reproduction and genetics, 39(3), 591-604.

Wen, J. Y., Liu, C. F., Chung, M. T., & Tsai, Y. C. (2022). Artificial intelligence model to predict pregnancy and multiple pregnancy risk following in vitro fertilization-embryo transfer (IVF-ET). Taiwanese journal of obstetrics and gynecology, 61(5), 837-846.

Wessel, J. A., Hunt, S., van Wely, M., Mol, F., & Wang, R. (2023). Alternatives to in vitro fertilization. Fertility and sterility, 120(3), 483-493.

Zhang, Y., Shen, L., Yin, X., & Chen, W. (2022). Live-birth prediction of natural-cycle in vitro fertilization using 57,558 linked cycle records: a machine learning perspective. Frontiers in endocrinology, 13, 838087.

Zhou, L., Liang, K., Li, M., Rong, C., Zheng, J., & Li, J. (2021). Metal elements associate with in vitro fertilization (IVF) outcomes in 195 couples. Journal of Trace Elements in Medicine and Biology, 68, 126810.

 

کلیدواژه‌ها
Livebirth prediction؛ Human Fertilization and Embryology Authority؛ Random Forest؛ Auto Label Encoder؛ Gradient Boosting؛ Ensemble machine learning algorithm
آمار
تعداد مشاهده مقاله: 158
تعداد دریافت فایل اصل مقاله: 128





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