Enhancing type 2 diabetes mellitus prediction by integrating metabolomics and tree-based boosting approaches
The integration of metabolomics profiling and XAI offers a promising approach to predicting T2DM. The use of tree-based algorithms, in particular KTBoost, provides a robust framework for analyzing complex datasets and improves the prediction accuracy of T2DM onset. Future research should focus on validating these biomarkers and models in larger, more diverse populations to solidify their clinical utility.
Authors
- Ahmet Kadir Arslan (ORCID: https://orcid.org/0000-0001-8626-9542)
- Fatma Hilal Yağın (ORCID: https://orcid.org/0000-0002-9848-7958)
- Erol Karaaslan (ORCID: https://orcid.org/0000-0002-8534-3680)
- Fahaid Al‐Hashem (ORCID: https://orcid.org/0000-0001-5795-9966)
- Abdulmohsen Algarni (ORCID: https://orcid.org/0000-0002-7556-958X)
- Luca Paolo Ardigò (ORCID: https://orcid.org/0000-0001-7677-5070)
Institutions
- Inonu University (TR)
- NLA University College (NO)
- King Khalid University (SA)
Publication Details
- Journal
- Frontiers in Endocrinology
- Published
- 2024-11-11
- DOI
- https://doi.org/10.3389/fendo.2024.1444282
- Citations
- 15
- Primary Topic
- Metabolomics and Mass Spectrometry Studies
- Type
- article
- Field-Weighted Citation Impact
- 3.51
Funders
- King Khalid University