Diagnosis of Thyroid Disease: Comparison of Adaptive Neural Fuzzy Inference System and Artificial Neural Network with the Logistic Regression Model

Abstract

Background: The development of data mining techniques and the adaptive neuro-fuzzy inference system (ANFIS) in the last few decades has made it possible to achieve accurate predictions in medical fields. Objectives: The present study aimed to use the ANFIS model, artificial neural network (ANN), and logistic regression to predict thyroid patients. Methods: This study aimed to predict thyroid disease using the UCI database, ANFIS and ANN models, and logistic regression. We only used four of its features as the input of the model and considered thyroid as a binary response (occurrence=1, non-occurrence=0) as the output of the model. Finally, three models were compared based on the accuracy and the area under the curve (AUC). Results: In this study, out of the extensive UCI database, which includes 3,772 samples and over 20 features, only five specific features were utilized. Data include 1,144 males and 2,485 females. The results of multiple logistic regression analysis demonstrated that free T4 index (FTI) and thyroid stimulating hormone (TSH) had a significant effect on thyroid. The ANFIS model had a higher accuracy (99%) compared to ANN (96%) and the logistic regression model (94%) in the prediction of thyroid. Conclusion: As evidenced by the obtained results, the forecasting performance of ANFIS is more efficient than other models. Moreover, the use of combined methods, such as ANFIS, to diagnose and predict diseases increases the accuracy of the model. Therefore, the results of this study can be used for screening programs to identify people at risk of thyroid disease.