Summary of An Integrated Optimization and Deep Learning Pipeline For Predicting Live Birth Success in Ivf Using Feature Optimization and Transformer-based Models, by Arezoo Borji et al.
An Integrated Optimization and Deep Learning Pipeline for Predicting Live Birth Success in IVF Using Feature Optimization and Transformer-Based Models
by Arezoo Borji, Hossam Haick, Birgit Pohn, Antonia Graf, Jana Zakall, S M Ragib Shahriar Islam, Gernot Kronreif, Daniel Kovatchki, Heinz Strohmer, Sepideh Hatamikia
First submitted to arxiv on: 27 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The developed artificial intelligence (AI) pipeline is designed to accurately predict live birth outcomes in IVF treatments by integrating feature selection methods such as principal component analysis (PCA) and particle swarm optimization (PSO), along with traditional machine learning-based classifiers like random forest (RF) and decision tree, as well as deep learning-based classifiers including custom transformer-based models. The pipeline uses anonymized data from the Human Fertilization and Embryology Authority (HFEA) between 2010-2018 to predict live birth success as a binary outcome. By combining PSO with the TabTransformer-based deep learning model, the study achieved an accuracy of 99.50% and AUC of 99.96%, demonstrating its potential to enhance personalized fertility treatments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The AI pipeline aims to improve IVF treatment outcomes by predicting live birth success using anonymized data from the Human Fertilization and Embryology Authority (HFEA). The researchers combined different feature selection methods with machine learning and deep learning models to achieve high accuracy. Their best results came from using particle swarm optimization for feature selection with a custom transformer-based model, which predicted live births accurately. |
Keywords
» Artificial intelligence » Auc » Decision tree » Deep learning » Feature selection » Machine learning » Optimization » Pca » Principal component analysis » Random forest » Transformer
