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

Frontiers in Artificial Intelligence, vol. 7, pp. 1392611

Abstract

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.

Authors 6

1. Seyed‐Ali Sadegh‐Zadeh

   University of Staffordshire

   Affiliation as printed

   Department of Computing, School of Digital, Technologies and Arts, Staffordshire University, Stoke-on-Trent, United Kingdom

2. Sanaz Khanjani

   Razi University

   Affiliation as printed

   Department of Computer Engineering, Razi University, Kermanshah, Iran

3. Shima Javanmardi Aachen Leiden Institute of Advanced Computer Science

   Leiden University

   Affiliation as printed

   Leiden Institute of Advanced Computer Science, Leiden University, Leiden, Netherlands

4. Bita Bayat

   Islamic Azad University of Malard

   Affiliation as printed

   Department of Computer Engineering, Artificial Intelligence, Islamic Azad University, Malard, Iran

5. Zahra Naderi

   Iran University of Medical Sciences

   Affiliation as printed

   Obstetrics and Gynaecology Department, Iran University of Medical Sciences, Tehran, Iran

6. Amir M. Hajiyavand corresponding

   University of Birmingham

   Affiliation as printed

   Department of Mechanical Engineering, School of Engineering, University of Birmingham, Birmingham, United Kingdom