Prediction and Classification Models for Hashimoto’s Thyroiditis Risk Using Clinical and Paraclinical Data

Background. One of the most prevalent autoimmune diseases and the main contributor to hypothyroidism in regions with sufficient iodine levels is Hashimoto’s thyroiditis. 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. Therefore, it is crucial to identify Hashimoto’s thyroiditis early on and do thyroid function tests. 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 the following machine learning models: Decision Tree, K-Nearest Neighbors, Extreme Gradient Boost, Support Vector Machine, as well as Artificial Neural Network and Deep Neural Network. 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. The suggested machine learning models are effective for diagnosing Hashimoto’s thyroiditis when combined with multiple factors. These findings advocate for screening for autoimmune thyroid disease in people with metabolic syndrome, breast cancer patients, and in women with surgically induced menopause.