Thyroid dysfunction diagnosis from routine laboratory tests based on machine learning

Abstract

AbstractApproximately 2.4 million patients in Japan need treatment for thyroid disease, including Graves’ disease and Hashimoto’s disease. However, only 450,000 of them are receiving treatment, and many patients with thyroid dysfunction remain largely overlooked. In this retrospective study, we aimed to screen patients with hyperthyroidism and hypothyroidism who would greatly benefit from prompt medical treatment. We examined routine laboratory finding data and machine learning algorithms to investigate whether such accurate and robust screening is possible to prevent overlooking and misdiagnosing thyroid dysfunction. We succeeded in developing a machine learning method to construct a classification model for detecting hyperthyroidism and hypothyroidism in patients using 11 routine laboratory tests. We collected electronic health records and medical checkup data from four hospitals in Japan. As a result of cross-validation and external evaluation, we achieved a high classification accuracy for the hyperthyroidism and hypothyroidism models.