Catalyzing IVF outcome prediction - exploring advanced machine learning paradigms for enhanced success rate prognostication

Aug 3, 2026

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Study focuses on improving In-Vitro Fertilization (IVF) success-rate prognostication by combining advanced machine learning methods with gynecological domain knowledge. The work analyzes comprehensive datasets spanning 2017–2018 and 2010–2016, training multiple algorithms including Logistic Regression, SVM, MLP, KNN, Gaussian NB, and ensemble learners such as Random Forest, AdaBoost, Logit Boost, RUS Boost, and RSM. Ensemble models deliver strong performance, with Logit Boost reaching 96.35% accuracy, emphasizing the role of demographics, infertility factors, and treatment protocols for decision support.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages18

Reading Time45 min

AccessFree to download

Introduction

IVF challenges and motivation

Data-driven AI/ML approach

Methods and modeling

Algorithms used

Ensemble learning strategies

Key findings and clinical implications

Demographics, infertility factors, protocols

Accuracy results and support use cases

Future work

Prospective studies and external validation

TYPE Original Research PUBLISHED 05 November 2024 DOI 10.3389/frai.2024.1392611 OPEN ACCESS EDITED BY Tim Hulsen, Philips (Netherlands), Netherlands REVIEWED BY Jayakumar Kaliappan, Vellore Institute of Technology, India Liliana Ibeth Barbosa Santillan, University of Guadalajara, Mexico Abhimanyu Banerjee, Illumina (United States), United States Mahdi-Reza Borna, Tarbiat Modares University, Iran *CORRESPONDENCE Amir M. Hajiyavand [a.hajiyavand@bham.ac.uk](a.hajiyavand@bham.ac.uk) RECEIVED 27 February 2024 ACCEPTED 23 October 2024 PUBLISHED 05 November 2024 CITATION Sadegh-Zadeh S-A, Khanjani S, Javanmardi S, Bayat B, Naderi Z and Hajiyavand AM (2024) Catalyzing IVF outcome prediction: exploring advanced machine learning paradigms for enhanced success rate prognostication. Front. Artif. Intell. 7:1392611 . doi: 10.3389/frai.2024.1392611 COPYRIGHT © 2024 Sadegh-Zadeh, Khanjani, Javanmardi, Bayat, Naderi and Hajiyavand. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Catalyzing IVF outcome prediction: exploring advanced machine learning paradigms for enhanced success rate prognostication Seyed-Ali Sadegh-Zadeh 1, Sanaz Khanjani 2, Shima Javanmardi3, Bita Bayat4, Zahra Naderi 5 and Amir M. Hajiyavand 6* 1 Department of Computing, School of Digital, Technologies and Arts, Staffordshire University, Stoke-on-Trent, United Kingdom, 2 Department of Computer Engineering, Razi University, Kermanshah, Iran, 3 Leiden Institute of Advanced Computer Science, Leiden University, Leiden, Netherlands, 4 Department of Computer Engineering, Artificial Intelligence, Islamic Azad University, Malard, Iran, 5Obstetrics and Gynaecology Department, Iran University of Medical Sciences, Tehran, Iran, 6 Department of Mechanical Engineering, School of Engineering, University of Birmingham, Birmingham, United Kingdom This study addresses the research problem of enhancing In-Vitro Fertilization (IVF) success rate prediction by integrating advanced machine learning paradigms with gynecological expertise. The methodology involves the analysis of comprehensive datasets from 2017 to 2018 and 2010–2016. Machine learning models, including Logistic Regression, Gaussian NB, SVM, MLP, KNN, and ensemble models like Random Forest, AdaBoost, Logit Boost, RUS Boost, and RSM, were employed. Key findings reveal the significance of patient demographics, infertility factors, and treatment protocols in IVF success prediction. Notably, ensemble learning methods demonstrated high accuracy, with Logit Boost achieving an accuracy of 96.35% . The implications of this research span clinical decision support, patient counseling, and data preprocessing techniques, highlighting the potential for personalized IVF treatments and continuous monitoring. The study underscores the importance of collaboration between gynecologists and data scientists to optimize IVF outcomes. Prospective studies and external validation are suggested as future directions, promising to further revolutionize fertility treatments and offer hope to couples facing infertility challenges. KEYWORDS in vitro fertilization, predictive modeling, machine learning, feature engineering, data preprocessing, hyperparameter tuning, algorithm selection, feature selection 1 Introduction In recent times, In-vitro fertilization (IVF) has emerged as a popular solution for addressing complications associated with infertility (Cascante et al., 2023). Infertility affects more than 80 million couples worldwide, prompting the need for effective reproductive interventions (Zafar et

FAQs

How does the study improve IVF success rate prediction?

It integrates advanced machine learning paradigms with gynecological expertise, using comprehensive datasets and multiple predictive models to generate more reliable prognoses.

Which machine learning models were evaluated in the research?

Models include Logistic Regression, Gaussian NB, SVM, MLP, KNN, and ensemble methods such as Random Forest, AdaBoost, Logit Boost, RUS Boost, and RSM.

What key factors were found to influence IVF success prediction?

Patient demographics, infertility factors, and treatment protocols were highlighted as significant contributors to prediction performance.

Aug 3, 2026

Upload by Oliver