Background. A theory that arose in recent years suggested that thyroid autoimmunity might be linked to low-grade chronic inflammation, which may cause cardiovascular comorbidities in the future, independent of thyroid function.

Methods. We gathered 129 volunteers, 104 of whom had been diagnosed with Hashimoto's thyroiditis, and 25 controls that did not have this disease. Secondly, we gathered 12 factors and examined their significant differences between controls and Hashimoto's thyroiditis patients. The clinical factors analyzed were age, family history of autoimmune thyroid disease, personal history of breast cancer, surgically induced menopause, diabetes mellitus type 2, and polycystic ovary syndrome. The following paraclinical parameters were examined: hypertriglyceridemia, anemia, hemoglobin and hematocrite levels. hypercholesterolemia abnormal liver function tests, hyperuricemia, and fasting hyperglycemia. For classification and regression, we assessed shallow machine learning models and neural networks.

Results. Extreme Gradient Boost had an area under the ROC curve of 87.5%, 80.8% accuracy, over 90% sensitivity, and over 80% specificity, making it the best model for binary classification. In terms of regression analysis, we discovered that the Deep Neural Network had a Pearson coefficient of 0.97 and an R-squared value of 0.94. A family history of autoimmune disease, a personal history of breast cancer, surgically induced menopause, anemia, hypertriglyceridemia, hyperuricemia, fasting hyperglycemia, and elevated alanine aminotransferase levels were all confirmed by statistical indicators used for the regression part of the study as significant risk factors for Hashimoto's thyroiditis.

Conclusions.These findings advocate for screening for autoimmune thyroid disease in people with metabolic syndrome, breast cancer patients, and in women with surgically induced menopause.