Diagnosing thyroid disorders: Comparison of logistic regression and neural network models - Shiva Borzouei (Journal of Family Medicine and Primary Care, 2020)
Background: The main goal of this study was to diagnose the two most common thyroid disorders, namely, hyperthyroidism and hypothyroidism, based on multinomial logistic regression and neural network models. In addition, the study evaluated the predictive ability of laboratory tests against the individual clinical symptoms score. Materials and Methods: In this study, the data from patients with thyroid dysfunction who referred to Imam Khomeini Clinic and Shahid Beheshti Hospital in Hamadan were collected. The data contained 310 subjects in one of three classes—euthyroid, hyperthyroidism, and hypothyroidism. Collected variables included demographics and symptoms of hypothyroidism and hyperthyroidism, as well as laboratory tests. To compare the predictive ability of the clinical signs and laboratory tests, different multinomial logistic regression and neural network models were fitted to the data. These models were compared in terms of the mean of the accuracy and area under the curve (AUC). Results: The results showed better performance of neural network model than multinomial logistic regression in all cases. The best predictive performance for logistic regression (with a mean accuracy of 91.4%) and neural network models (with a mean accuracy of 96.3%) was when all variables were included in the model. In addition, the predictive performance of two models based on symptomatic variables was superior to laboratory variables. Conclusions: Both neural network and logistic regression models have a high predictive ability to diagnose thyroid disorder, although neural network performance is better than logistic regression. In addition, as achieving less error prediction model has always been a matter of concern for researchers in the field of disease diagnosis, predictive nonparametric techniques, such as neural networks, provide new opportunities to obtain more accurate predictions in the field of medical research.