A Proactive Explainable Artificial Neural Network Model for the Early Diagnosis of Thyroid Cancer

Abstract

Early diagnosis of thyroid cancer can reduce mortality, and can decrease the risk of recurrence, side effects, or the need for lengthy surgery. In this study, an explainable artificial neural network (EANN) model was developed to distinguish between malignant and benign nodules and to understand the factors that are predictive of malignancy. The study was conducted using the records of 724 patients who were admitted to Shengjing Hospital of China Medical University. The dataset contained the patients’ demographic information, nodule characteristics, blood test findings, and thyroid characteristics. The performance of the model was evaluated using the metrics of accuracy, sensitivity, specificity, F1 score, and area under the curve (AUC). The SMOTEENN combined sampling method was used to correct for a significant imbalance between malignant and benign nodules in the dataset. The proposed model outperformed a baseline study, with an accuracy of 0.99 and an AUC of 0.99. The proposed EANN model can assist health care professionals by enabling them to make effective early cancer diagnoses.