| Celiac Disease is an autoimmune disease with a prevalence between 1-1.8% worldwide. As it is believed to be highly underdiagnosed, new approaches and protocols are being explored for this purpose. In this paper, we aim to use artificial intelligence models to support the medical diagnosis of Celiac Disease from clinical symptoms and biomarkers. Through our experiments, we identified accurate models able to predict Celiac Disease diagnosis from noninvasive clinical indicators with an accuracy of 0.97, and the specific type of disease from 6 different classes with an accuracy of 0.92. Thus, even with the limitations of the present study, we conclude that machine learning can help support the diagnosis of Celiac Disease and provide an accurate identification of its type. We encourage researchers to collect and share data on Celiac Disease as it is necessary for improving the intelligent models and their robustness. |
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