| Title: | A stacking ensemble machine learning model to predict alpha-1 antitrypsin deficiency-associated liver disease clinical outcomes based on UK Biobank data |
| Journal: | Scientific Reports |
| Published: | 11 Oct 2022 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/36220873/ |
| DOI: | https://doi.org/10.1038/s41598-022-21389-9 |
| URL: | https://www.nature.com/articles/s41598-022-21389-9.pdf |
| Citations: | 5 (5 in last 2 years) as of 8 Aug 2024 |
Publication 8127
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Abstract
Alpha-1 antitrypsin deficiency associated liver disease (AATD-LD) is a rare genetic disorder and not well-recognized. Predicting the clinical outcomes of AATD-LD and defining patients more likely to progress to advanced liver disease are crucial for better understanding AATD-LD progression and promoting timely medical intervention. We aimed to develop a tailored machine learning (ML) model to predict the disease progression of AATD-LD. This analysis was conducted through a stacking ensemble learning model by combining five different ML algorithms with 58 predictor variables using nested five-fold cross-validation with repetitions based on the UK Biobank data. Performance of the model was assessed through prediction accuracy, area under the receiver operating characteristic (AUROC), and area under the precision-recall curve (AUPRC). The importance of predictor contributions was evaluated through a feature importance permutation method. The proposed stacking ensemble ML model showed clinically meaningful accuracy and appeared superior to any single ML algorithms in the ensemble, e.g., the AUROC for AATD-LD was 68.1%, 75.9%, 91.2%, and 67.7% for all-cause mortality, liver-related death, liver transplant, and all-cause mortality or liver transplant, respectively. This work supports the use of ML to address the unanswered clinical questions with clinically meaningful accuracy using real-world data.4 Authors
- Linxi Meng
- Will Treem
- Graham A. Heap
- Jingjing Chen
1 Application
| Application ID | Title |
|---|---|
| 26041 | Large-Scale Sequencing in the UK Biobank to Facilitate Gene Discovery, Genome Sciences, and Precision Medicine |
Enabling scientific discoveries that improve human health