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

Area of Science:

• Reproductive Medicine
• Artificial Intelligence in Healthcare
• Biomedical Data Science

Background:

• In-Vitro Fertilization (IVF) success rates vary significantly.
• Accurate prediction of IVF outcomes is crucial for patient counseling and treatment planning.
• Integrating machine learning with clinical expertise can enhance predictive accuracy.

Purpose of the Study:

• To improve the prediction of In-Vitro Fertilization (IVF) success rates.
• To evaluate the efficacy of various machine learning models, including ensemble methods, for IVF outcome prediction.
• To identify key factors influencing IVF success through data analysis.

Main Methods:

• Analysis of comprehensive IVF datasets from 2010-2016 and 2017-2018.
• Implementation and comparison of machine learning models: Logistic Regression, Gaussian NB, SVM, MLP, KNN.
• Application of ensemble learning techniques: Random Forest, AdaBoost, Logit Boost, RUS Boost, RSM.

Main Results:

• Patient demographics, infertility factors, and treatment protocols are significant predictors of IVF success.
• Ensemble learning methods demonstrated superior accuracy in predicting IVF outcomes.
• Logit Boost achieved a high accuracy of 96.35% in predicting IVF success.

Conclusions:

• Machine learning, especially ensemble methods, can significantly enhance IVF success prediction.
• The findings support the development of personalized IVF treatments and improved clinical decision support.
• Collaboration between gynecologists and data scientists is vital for optimizing fertility treatments.