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Comparative study of machine learning approaches integrated with genetic algorithm for IVF success prediction.

Shirin Dehghan1, Reza Rabiei1, Hamid Choobineh2

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Machine learning models, particularly AdaBoost with Genetic Algorithm (GA) feature selection, accurately predict In Vitro Fertilization (IVF) success rates up to 89.8%. Key factors include female age and embryo quality, aiding personalized treatment.

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Area of Science:

  • Reproductive Medicine
  • Artificial Intelligence
  • Biomedical Data Science

Background:

  • In Vitro Fertilization (IVF) success rates remain around 30%, necessitating improvements due to significant patient impact.
  • Enhancing IVF outcomes is critical for addressing the emotional, financial, and health burdens faced by infertile couples.

Purpose of the Study:

  • To develop and compare machine learning models for predicting IVF success.
  • To identify key predictors of successful IVF outcomes.

Main Methods:

  • Evaluated five machine learning algorithms: Random Forest, Artificial Neural Network (ANN), Support Vector Machine (SVM), Recursive Partitioning and Regression Trees (RPART), and AdaBoost.
  • Utilized Genetic Algorithm (GA) for feature selection to enhance model performance and identify crucial predictive factors.

Main Results:

  • AdaBoost combined with GA achieved the highest prediction accuracy at 89.8%.
  • Random Forest with GA also showed strong performance (87.4% accuracy).
  • GA significantly improved all tested classifiers, highlighting the importance of feature selection. Identified ten key predictors including female age, AMH, endometrial thickness, sperm count, and oocyte/embryo quality indicators.

Conclusions:

  • Machine learning models, especially with feature selection, show significant potential for improving IVF outcome prediction.
  • These predictive capabilities can assist clinicians in developing personalized treatment strategies for IVF patients.
  • Further research and clinical validation are recommended to integrate these models into standard IVF practice.