用优化特征+Transformer模型，精准预测试管婴儿成功率。
An Integrated Optimization and Deep Learning Pipeline for Predicting Live Birth Success in IVF Using Feature Optimization and Transformer-Based Models
- 结合粒子群优化与注意力机制，筛选关键生育指标。
- 准确率达99.50%，AUC达99.96%，性能显著领先。
- 适合辅助生殖领域临床决策与个性化治疗研究。
体外受精（IVF）是广泛应用的辅助生殖技术，但其成功预测仍因临床、人口统计及操作因素的复杂交互而困难。本研究构建了一套强大的人工智能预测管道，基于2010至2018年来自人类受精与胚胎学管理局（HFEA）的匿名数据，以二分类任务（成功/失败）评估活产预测性能。通过整合主成分分析（PCA）与粒子群优化（PSO）等特征选择方法，以及随机森林（RF）、决策树等传统机器学习模型，和自研Transformer模型、带有注意力机制的TabTransformer模型等深度学习模型进行对比。结果表明，采用PSO进行特征选择并结合TabTransformer模型时表现最佳，准确率达99.50%，AUC为99.96%，展现出卓越的预测能力。本研究建立了一个高精度的AI预测管道，具有提升个性化辅助生殖治疗的潜力。
原文摘要 · Abstract (English)
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.
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