Machine Learning Models for Predicting Celiac Disease Based on Non-invasive Clinical Symptoms
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-63211-2_12
Reference24 articles.
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2. Caio, G., Volta, U., Sapone, A., Leffler, D.A., De Giorgio, R., Catassi, C., Fasano, A.: Celiac disease: a comprehensive current review. BMC Med. 17, 1–20 (2019)
3. Camarca, M.E., et al.: Celiac disease in type 1 diabetes mellitus. Ital. J. Pediatr. 38(1), 1–7 (2012)
4. Carreras, J.: Artificial intelligence analysis of celiac disease using an autoimmune discovery transcriptomic panel highlighted pathogenic genes including btla. In: Healthcare, vol. 10, p. 1550. MDPI (2022)
5. Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016, pp. 785–794. Association for Computing Machinery, New York (2016). https://doi.org/10.1145/2939672.2939785
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