An Integrated Optimization and Deep Learning Pipeline for Predicting Live Birth Success in IVF Using Feature Optimization and Transformer-Based Models

TL;DR

This paper presents a highly accurate AI pipeline that combines feature optimization and transformer-based deep learning models to predict live birth success in IVF treatments, potentially improving personalized fertility care.

Contribution

It introduces an integrated pipeline using feature selection and transformer models, achieving unprecedented prediction accuracy for IVF success.

Findings

Best model achieved 99.50% accuracy

Transformer-based models outperform traditional classifiers

Feature optimization significantly improves prediction performance

Abstract

In vitro fertilization (IVF) is a widely utilized assisted reproductive technology, yet predicting its success remains challenging due to the multifaceted interplay of clinical, demographic, and procedural factors. This study develops a robust artificial intelligence (AI) pipeline aimed at predicting live birth outcomes in IVF treatments. The pipeline uses anonymized data from 2010 to 2018, obtained from the Human Fertilization and Embryology Authority (HFEA). We evaluated the prediction performance of live birth success as a binary outcome (success/failure) by integrating different feature selection methods, such as principal component analysis (PCA) and particle swarm optimization (PSO), with different traditional machine learning-based classifiers including random forest (RF) and decision tree, as well as deep learning-based classifiers including custom transformer-based model and a tab transformer model with an attention mechanism. Our research demonstrated that the best performance was achieved by combining PSO for feature selection with the TabTransformer-based deep learning model, yielding an accuracy of 99.50% and an AUC of 99.96%, highlighting its significant performance to predict live births. This study establishes a highly accurate AI pipeline for predicting live birth outcomes in IVF, demonstrating its potential to enhance personalized fertility treatments.