The thyroid gland is an endocrine gland creating thyroid hormone. It is shaped like a butterfly and positioned in the front of the neck. Thyroid hormone involves the regulation of metabolism and various problems can occur in the gland. It can create either too little or too much hormone (hypothyroidism or hyperthyroidism). The former condition causes fatigue, weight gain and intolerance to cold temperature, whereas the latter leads to anxiety, weight loss and sensitivity to heat. Also, malignant cells can develop there (thyroid cancer) [1, 2]. These disorders, thyroid disease, has been a leading cause of disease burden in the world [3, 4, 5, 6]. The number of individuals with thyroid disease is estimated to be 200 million in the world [3], whereas the incidence and mortality of thyroid cancer registered rapid growths of 169% and 87% during 1990–2017, i.e., 95,030 and 22,070 to 255,490 and 41,240, respectively [4]. Hypothyroidism is reported to cause significant disease burden and direct, morbidity and mortality cost, as well [5, 6]. It has various risk factors and many of them are still unknown. Its diagnosis and prognosis are considered to be quite challenging given that its symptoms are very similar with other diseases such as depression [1, 2, 3]. It is not surprising that there exists a high degree of variation among clinical experts in terms of its diagnosis and prognosis. In this context, more research is to be done on this important topic. Recently, on the other hand, the terms “deep learning”, “machine learning” and “artificial intelligence” have attracted great attention all over the globe. For instance, their Google trends recorded ten-fold expansions from 10 to 100 during 2013–2018. Artificial intelligence can be defined as “the capability of a machine to imitate intelligent human behavior” (the Merriam-Webster dictionary). The definition of machine learning can be a division of artificial intelligence to “extract knowledge from large amounts of data” [7].

Six common machine learning algorithms are the decision tree, the naïve Bayesian predictor, the random forest, the support vector machine, the artificial neural network, and the deep neural network (deep learning). A decision tree has three components: an intermediate node (a test on an independent variable), a branch (an outcome of the test) and a terminal node (a value of the dependent variable). A naïve Bayesian predictor makes an early diagnosis based on Bayes’ theorem, which states that the probability of the dependent variable given certain values of independent variables comes from the probabilities of the independent variables given a certain value of the dependent variable. A random forest is a collection of many decision trees with a majority vote on the dependent variable (“bootstrap aggregation”). Let us take a random forest with 1000 decision trees as an example. Here, the algorithm samples 1000 training sets with replacements, trains 1000 decision trees with the 1000 training sets, makes 1000 predictions with the 1000 decision trees, and takes a majority vote on the dependent variable. A support vector machine originates a line or space called a “hyperplane” (a collection of “support vectors”). The hyperplane divides data with the greatest distance between different sub-groups [7].

An artificial neural network is a network of “neurons”, i.e., information units combined through weights. Usually, the artificial neural network has one input layer, one, two or three intermediate layers and one output layer. Neurons in a previous layer connect with “weights” in the next layer and these weights represent the strengths of connections between neurons in a previous layer and their next-layer counterparts. This process starts from the input layer, continues through intermediate layers and ends in the output layer (feedforward operation). Then, learning happens: these weights are accommodated based on how much they contributed to the loss, a difference between the actual and predicted final outputs. This process starts from the output layer, continues through intermediate layers and ends in the input layer (backpropagation operation). The two operations are replicated until a certain expectation is met regarding the accurate diagnosis of the dependent variable. In other words, the performance of the artificial neural network improves as long as its learning continues. Finally, a deep neural network is an artificial neural network with a large number of intermediate layers, e.g., 5, 10 or even 1000. The deep neural network is called “deep learning” given that learning “deepens” through numerous intermediate layers [8].

Traditional research considers a limited scope of predictors for the early diagnosis of disease, whereas adopting logistic regression with an unrealistic assumption of ceteris paribus, i.e., “all the other variables staying constant”. In this context, emerging literature uses artificial intelligence for the early diagnosis of disease, e.g., arrhythmia [8], birth outcome [9, 10, 11, 12, 13, 14], cancer [15, 16, 17, 18, 19], comorbidity [20, 21, 22], menopause [23] and temporomandibular disease [24, 25]. It does not require unrealistic assumptions of “all the other variables staying constant” while managing to analyze which predictors are more important for the early diagnosis of the dependent variable. The purpose of this study is to review the recent progress of machine learning for the early diagnosis of thyroid disease.

Twenty original studies were selected for review out of 33 original studies in PubMed with the search terms “thyroid” (title) and “random forest” (abstract). The inclusion criteria of this review were: (1) the intervention(s) of the decision tree, the naïve Bayesian predictor, the random forest, the support vector machine and/or the artificial neural network; (2) the outcome(s) of accuracy and/or the area under the receiver operating characteristic curve for the early diagnosis of thyroid disease; (3) the publication year of 2020 or later; and (4) the publication language of English. The following summary measures were adopted: machine learning methods, sample size, data type, performance measures and important attributes (predictors). Here, accuracy can be defined as the proportion of correct predictions over all observations, while the area under the receiver operating characteristic curve (AUC) can be defined as the area under the plot of the true positive rate (sensitivity) against the false positive rate (1-specificity) at various threshold settings. The exclusion criteria of this review was that thyroid disease is an independent variable (attribute) instead of the dependent variable.

This study reviewed original studies including the random forest and the four other machine learning methods: The twenty original studies were selected out of 33 original studies in PubMed with the search terms “thyroid” (title) and “random forest” (abstract). This study put more focus on the random forest for two reasons. Firstly, it has the advantage of rigorous cross validation from “bootstrap aggregation”: it is a collection of many decision trees with a majority vote on the dependent variable. For example, a random forest with 1000 decision trees samples 1000 training sets with replacements, trains 1000 decision trees with the 1000 training sets, makes 1000 predictions with the 1000 decision trees, and takes a majority vote on the dependent variable. In other words, the random forest with 1000 decision trees uses rigorous 1000-fold cross validation and this explains why it usually shows the best performance together with boosting and neural network approaches [7, 15, 17, 19]. Secondly, the random forest can analyze which predictors are more important for the early diagnosis of a disease [7, 15, 17, 19]. But another method can be more accurate and more appropriate than the random forest in certain circumstances. Little research has been done and more effort is to be made on this topic.

This study reveals that random forest variable importance would vary across different types of data for the early diagnosis of thyroid disease. The following attributes would be important variables in the case of numeric data: (1) clinical stage, marital status, histological type, age, nerve injury symptom, economic income and surgery type for predicting the quality of life 3 months after thyroid cancer surgery; tumor diameter, symptoms and extrathyroidal extension for predicting the local recurrence of differentiated thyroid carcinoma. Likewise, the list of important attributes in the case of genomic data would include: (1) RNA feasures including ADD3-AS1 (downregulation), MIR100HG (downregulation), FAM95C (downregulation), MORC2-AS1 (downregulation), LINC00506 (downregulation), ST7-AS1 (downregulation), LOC339059 (downregulation), MIR181A2HG (upregulation), FAM181A-AS1 (downregulation), LBX2-AS1 (upregulation), BLACAT1 (upregulation), hsa-miR-9-5p (downregulation), hsa-miR-146b-3p (upregulation), hsa-miR-199b-5p (downregulation), hsa-miR-4709-3p (upregulation), hsa-miR-34a-5p (upregulation) and hsa-miR-214-3p (downregulation) for the early diagnosis of papillary thyroid carcinoma; (2) gut microbiota RNA features such as veillonella, paraprevotella, neisseria and rheinheimera for the early diagnosis of hypothyroidism. In a similar vein, the following ultrasound features are expected to request due attention for predicting the malignancy of thyroid nodules: wreath-shaped feature, micro-calcification and strain ratio. As noted before, machine learning is a data-driven method and more study is to be done for greater external validity. However, the findings above would present useful guidelines on the effective application of random forest variable importance across a variety of data modes for the early diagnosis of thyroid disease in future research.

But current studies on the early diagnosis of thyroid disease based on machine learning has the following limitations. Firstly, many studies adopted cross-sectional data and employing longitudinal data would strengthen the performance of machine learning. Secondly, many studies used data with small sizes in single centers. Using big data (e.g., national health insurance claims data) would make valuable contributions for this area. Thirdly, most studies did not consider possible mediating effects among predictors. Fourthly, some studies reported accuracy or the AUC below 70.0 and these results would not be appropriate as diagnostic tests. Fifthly, binary categories (no, yes) are popular now but they can be refined to multiple categories with more clinical insights. Sixthly, combining different types of machine learning approaches for different types of thyroid data would bring new innovations in many aspects. Finally, it can be noted that this study did not use meta-analysis because different studies would have different diagnostic aims.

This article reviewed the recent progress of machine learning for the early diagnosis of thyroid disease. This review demonstrates that machine learning provides an effective, non-invasive decision support system for early diagnosis of thyroid disease.

AUC, area under the receiver operating characteristic curve.

KSL and HP contributed to research design, data collection, analysis and interpretation, as well as manuscript writing, editing and review. KSL and HP approved the final version of the manuscript.

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This research was supported by the Ministry of Science and ICT of South Korea under the Information Technology Research Center support program supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation) (IITP-2018-0-01405).

The authors declare no conflict of interest. KSL and HP are serving as the guest editors of this journal. We declare that KSL and HP had no involvement in the peer review of this article and have no access to information regarding its peer review. Full responsibility for the editorial process for this article was delegated to AP.