Artificial intelligence-enhanced in-vitro fertilization outcome prediction for the Indian subpopulation: integrating pre-treatment parameters and Bayesian-optimized ensemble techniques

Abhishek Sengupta; Gaurav Majumdar; Alakto Choudhury; Payal Gupta; Deepak Modi; Priyanka Narad

doi:10.18203/2394-6040.ijcmph20251387

Authors

Abhishek Sengupta Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, Uttar Pradesh, India https://orcid.org/0000-0002-3958-3675
Gaurav Majumdar Centre of IVF and Human Reproduction, Sir Ganga Ram Hospital, Rajinder Nagar, New Delhi, India
Alakto Choudhury Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, Uttar Pradesh, India https://orcid.org/0000-0002-4340-2179
Payal Gupta Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, Uttar Pradesh, India https://orcid.org/0000-0001-9965-3789
Deepak Modi Molecular and Cellular Biology Laboratory, ICMR-National Institute for Research in Reproductive and Child Health, Mumbai, Maharashtra, India https://orcid.org/0000-0002-4230-4219
Priyanka Narad Indian Council of Medical Research, Ansari Nagar, New Delhi, India https://orcid.org/0000-0001-7317-654X

DOI:

https://doi.org/10.18203/2394-6040.ijcmph20251387

Keywords:

In vitro fertilization, Machine learning, Cloud application, Machine learning web application, Prediction modelling, Assisted reproductive technology

Abstract

Background: In-vitro fertilization (IVF) outcomes, particularly their socioeconomic impact, are a major concern for Indian couples. Predicting success using pre-treatment parameters can improve clinical decision-making. This study develops and validates Bayesian-optimized voting-ensemble (BoVe), a novel machine learning (ML) algorithm, to enhance predictive accuracy for live birth outcomes.

Methods: Clinical records from 2,908 IVF patients, encompassing 79 parameters-including maternal age, body mass index (BMI), Anti-Mullerian hormone (AMH) levels, number of IVF cycles, infertility type, and sperm parameters were analyzed following rigorous data preprocessing. The dataset was cleaned, transformed, and split 80:20 for training and validation. BoVe was evaluated against traditional ML models based on key performance metrics.

Results: BoVe identified AMH levels >3.5 ng/mL, BMI <23, and maternal age <35 as strong predictors of live birth in female patients. Male sperm parameters significantly influenced success rates. Compared to conventional ML models, BoVe achieved superior predictive performance with an ROC-AUC score of 0.93 and accuracy of 0.87, demonstrating robust effectiveness. Additionally, an AI-powered web application was developed for cloud-based fertility guidance, providing personalized recommendations based on patient parameters.

Conclusions: The BoVe model offers a highly accurate, population-specific approach to IVF prediction, surpassing previously published algorithms. Its integration into clinical workflows can enhance pre-treatment counseling, empower couples with data-driven reproductive insights, and improve success rates through personalized interventions.

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