Identifying top ten predictors of type 2 diabetes through machine learning analysis of UK Biobank data
Abstract The study aimed to identify the most predictive factors for the development of type 2 diabetes. Using an XGboost classification model, we projected type 2 diabetes incidence over a 10-year horizon. We deliberately minimized the selection of baseline factors to fully exploit the rich dataset...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-52023-5 |