Development and preliminary validation of a machine learning system for thyroid dysfunction diagnosis based on routine laboratory tests.

Background: Approximately 2.4 million patients in Japan would benefit from 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 develop and conduct preliminary testing on a machine learning method for screening patients with hyperthyroidism and hypothyroidism who would benefit from prompt medical treatment.
Methods: We collected electronic medical records and medical checkup data from four hospitals in Japan. We applied four machine learning algorithms to construct classification models to distinguish patients with hyperthyroidism and hypothyroidism from control subjects using routine laboratory tests. Performance evaluation metrics such as sensitivity, specificity, and the area under receiver operating characteristic (AUROC) were obtained. Techniques such as feature importance were further applied to understand the contribution of each feature to the machine learning output.
Results: The results of cross-validation and external evaluation indicated that we achieved high classification accuracies (AUROC = 93.8% for hyperthyroidism model and AUROC = 90.9% for hypothyroidism model). Serum creatinine (S-Cr), mean corpuscular volume (MCV), and total cholesterol were the three features that were most strongly correlated with the hyperthyroidism model, and S-Cr, lactic acid dehydrogenase (LDH), and total cholesterol were correlated with the hypothyroidism model. Conclusions: We demonstrated the potential of machine learning approaches for diagnosing the presence of thyroid dysfunction from routine laboratory tests. Further validation, including prospective clinical studies, is necessary prior to application of our method in the clinic.

Introduction

Thyroid dysfunction is a leading endocrine disorder with major health implications, including an increased risk of heart disease and hypercholesterolemia. One of the greatest challenges in thyroid dysfunction treatment is not to overlook or misdiagnose these diseases. Thyroid hormone excess and deficiency are frequently misunderstood and are too often overlooked and misdiagnosed[1]. For hyperthyroidism, the diagnosis may be delayed or missed because some symptoms can be easily attributed to other conditions, such as stress[2], and are often mistaken for cardiac disease or gastrointestinal malignancies. Hypothyroidism can present with nonspecific constitutional and neuropsychiatric complaints, and patients with hypothyroidism are often misdiagnosed with dementia, cardiac disease, liver disease, or hyperlipidemia and therefore do not receive proper treatment[3]. The American Association of Clinical Endocrinologists has estimated that in the United States, ~4.78% of the population has misdiagnosed thyroid dysfunction[4], and the authors argue that ~15 million adults are estimated to have unrecognized thyroid disease[5]. In Japan, it is estimated that ~2.4 million patients need treatment for thyroid disease[6]. However, only ~450,000 of them are receiving treatment. Thus, patients with thyroid dysfunction are frequently overlooked and misdiagnosed[6].

Hyperthyroidism is a condition that occurs due to excessive production of thyroid hormones. The first step to diagnose hyperthyroidism is to measure thyroid-stimulating hormone (TSH), free thyroxine (FT4) and free triiodothyronine (FT3)[7]. In contrast, hypothyroidism is a condition in which serum thyroid hormones are decreased. Typical diseases involving hypothyroidism include Hashimoto’s disease and are diagnosed by anti-thyroid antibody tests such as anti-thyroid peroxidase antibody (TPO) and anti-thyroglobulin antibody (TgAb)[7]. Despite their clinical significance, thyroid function tests and anti-thyroid antibody tests are not included in Japanese national health checkups.

As a popular and effective approach to predictive analytics, machine learning is highly regarded due to its success in diagnosis, prediction, and choice of treatment. Recently, an emerging technique in the field of medical informatics has employed machine learning to accurately derive insights from medical records to support clinical screening and to predict disease misdiagnosis[8]. For instance, a study emphasized the superiority of machine-learning technology for predicting cardiovascular risk from routine clinical data[9]. In another study, the incidence of myocardial infarction or cerebral infarction was predicted using the results of a health checkup[10]. Numerous studies have also attempted to assess the efficacy of detecting misdiagnosed diseases, including thyroid dysfunction[11-17]. Aoki et al.[16,17] found that there were strong, multiple correlations between a set of routine clinical parameters and FT4 in patients with both overt hyperthyroidism and overt hypothyroidism. These studies used pattern recognition methods such as neural networks and predicted the likelihood of thyroid dysfunction from a set of routine clinical tests.

Despite such efforts, there are still several concerns regarding machine-learning applications in the diagnosis of disease. These include the issues of data cleaning, missing value completion, dysfunction labeling criteria, the integration of multiple hospital datasets, and the validation and interpretation of machine-learning models. In this study, we developed an explainable diagnosis support system using machine-learning algorithms to identify thyroid dysfunction with routine clinical data, and demonstrated the potential to improve medical screening and prevent overlooking and misdiagnosing thyroid dysfunction. High accuracy was achieved in the discrimination of evident hyperthyroidism and hypothyroidism using 23 routine laboratory tests, and these features can be useful for individuals who are not thyroid disease specialists.

Methods

Data source

In the present study, we acquired laboratory finding datasets from different clinical university medical institutions in Japan, including Wakayama Medical University Hospital, Gunma University Hospital, Hidaka Hospital, and Kuma Hospital. The anonymized EMRs included age, sex, diagnosis codes for insurance billing, prescribed drugs, and biochemical test results.

A sample of 176,727 subjects aged 13 to 88 from different regions in Japan between 2004 and 2019 were included in our study, as illustrated in Table 1. Among the four institutions, Wakayama Medical University Hospital and Gunma University Hospital are hospitals affiliated with a medical college, Hidaka Hospital is a regional medical care support hospital, and Kuma Hospital is a hospital specializing in thyroid diseases. The data of the 176,727 subjects consisted of physician evaluations, prescriptions, clinical examinations, and laboratory findings. The physician evaluations addressed medical history, medication use, and differential diagnosis, among other topics. If a subject was prescribed medication, the name and dose of the prescription were recorded. The examinations involved anthropometric measurements and laboratory tests, among others. The institutional ethics review boards of the four institutions at which the study was conducted gave their approval (Approval Numbers: Wakayama Medical University Hospital: 2301, Hidaka Hospital: 257, Gunma University Hospital: HS2018-245, Kuma Hospital: 20180208-4). All methods were performed in accordance with the relevant guidelines and regulations, including ethical guidelines for Medical and Health Research Involving Human Subjects presented by the Ministry of Health, Labor and Welfare in Japan. According to the ethical guidelines for Medical and Health Research Involving Human Subjects, with this study design, written informed consent is not required, but we widely disclosed the outline of our study and provided opportunities for unenrollment.

Table 1

Summary of the data from each institution.

Institution	Wakayama Medical University	Gunma University	Hidaka Hospital	Kuma Hospital
Number of prescriptions	8,249,286	34,561,268	23,450	61,590
Number of patients	14,249	27,133	10,482	124,863
Average age	60.9	51.7	47.7	50.3
Male/female ratio	1.03 (5,888/5,723)	0.53 (8,143/15,296)	1.82 (15,125/8,325)	0.21
Data period	2010–2018	2004–2019	2004–2007	2007–2020

The demographic summary is shown for each institution. “Number of prescriptions” represents the number of prescription records in each dataset, and “Number of patients” represents the number of patients in each dataset. “Average age”, and “male/female ratio” and “data period” represent the demographic summary of the patients in each institution.

The K-nearest neighbor (KNN) algorithm was used to predict and complement the missing values, with k set to 3 in the data filling process. A previous study[11] reported the KNN algorithm to substantially increase the number of applicable subjects. Compared with missing value deletion, the KNN algorithm is easily applied, performs well for nonparametric datasets, and provides a larger sample size. Furthermore, since the age and sex distributions were different among the institutions, as shown in Table 1, we also conducted random undersampling to address these differences. From this dataset, the model was constructed using thyroid patient data from Wakayama Medical University and Gunma University and control group data from Hidaka Hospital and was evaluated using cross-validation. To validate the external data, the model was also evaluated on the dataset from Kuma Hospital.

Construction of the machine-learning model

As shown in Table 2, four verification items were devised in this study to improve the performance of our machine-learning model. The criteria of data labeling and the combination of multiple institutions were evaluated first. Then, four different machine-learning algorithms and three sets of input features were evaluated to achieve the best performance of our thyroid dysfunction classification models.

Table 2

List of verification items.

No.	Verification item	Option
1	Training data labeling	Thyroid function test criterion	Prescription criterion
2	Institution combination (for patient data and control group data)	Institution combination 1 (Inst. comb. 1)	Institution combination 2 (Inst. comb. 2)	Institution combination 3 (Inst. comb. 3)	External
3	Machine-learning algorithm	GBDT	SVM	Logistic regression	ANN
4	Input features	Feature set 1	Feature set 2

Verification items in this study are categorized into four groups: “Training data labeling”, “Institution combination”, “Machine-learning algorithm”, and “Input features”. Each category contains several specific verification options and was verified in our experiments.

Data labeling criterion

According to the guidelines of the Japanese Thyroid Association for the diagnosis of hyperthyroidism and hypothyroidism, if thyroid disorder is suspected from the clinical findings, first, a thyroid function test (TSH and FT4 measurement) is conducted, and on the basis of these results, thyroid disorder is classified into three categories—hyperthyroidism, hypothyroidism, or euthyroidism[7]. Therefore, we devised and compared the performance of two data labeling criteria.

We first devised the labeling criterion by using the result of the thyroid function test as a reference (hereinafter referred to as the “thyroid function test criterion”). Specifically, in the dataset from Wakayama Medical University, FT4 and TSH were measured with ECLusys kits (Roche Diagnostics GmbH, Mannheim, Germany). TSH < 0.5 and FT4 > 1.7 were defined as overt hyperthyroidism, and TSH > 5.0 and FT4 < 0.9 were defined as overt hypothyroidism (TSH unit: μIU/mL; FT4 unit: ng/dL). In the dataset from Gunma University, in which FT4 and TSH were measured with the Architect kits, TSH < 0.35 and FT4 > 1.48 were defined as overt hyperthyroidism, and TSH > 4.94 and FT4 < 0.7 were defined as overt hypothyroidism.

Data for the control group were extracted from the third institution, Hidaka Hospital, and consisted of the test results from regular medical examinations. We extracted comprehensive medical examination data for subjects who did not have any symptoms, suggesting thyroid dysfunction or abnormal values in the laboratory tests of TSH and FT4. The normal ranges were set to 0.34–3.88 μIU/mL for TSH and 0.95–1.74 ng/dL for FT4. Random undersampling was conducted for the control group in such a way that the sample size of the control group was equivalent to the sizes of the hyperthyroidism and hypothyroidism groups.

The thyroid function test criterion required both TSH and FT4 test results, but a small number of patient records had both of these results. Therefore, as an alternative solution, we devised another criterion of labeling the training data according to the presence of a prescription for thyroid disorder (hereinafter referred to as the “prescription criterion”). Specifically, the use of the prescription criterion satisfies the following conditions: (a) it includes patient records with standard prescribed medications for thyroid dysfunction (including thiamazole, propylthiouracil, and potassium iodide for the hyperthyroidism group and levothyroxine and thyronamine for the hypothyroidism group) obtained at the patients’ first visits, (b) it includes patients not diagnosed with thyroid nodules, (c) it includes patient records containing laboratory findings obtained within four weeks after the patient’s first prescription, and (d) it excludes records with missing values for more than half of our selected features. Since the age distributions were different among the institutions, as shown in Table 1, we also conducted data sampling to address these differences. Between the two criteria designed in this study, we focused on evaluating thyroid function test criteria as the gold standard criteria while exploring the effect of the prescription criteria, which may benefit from a larger dataset.

In machine learning, a control group is generally used as a negative label. Since hyperthyroidism and hypothyroidism are conditions of thyroid dysfunction, both often express similar symptoms and effects on some routine laboratory findings (e.g., Hb is decreased in both hyperthyroidism and hypothyroidism patients). Therefore, we considered the confounding of hyperthyroidism and hypothyroidism as “crosstalk” and refined the labeling criteria in such a way that the negative label was set as both the healthy subjects of the control group and the patients with the opposite type of thyroid dysfunction. For instance, in the data labeling process of the hyperthyroidism classification model, the hyperthyroidism group was set as a positive label, whereas both healthy subjects in the control group and hypothyroidism patients were set as a negative label.

Integrating multiple hospital datasets

The demographics were different among the three institutions in different districts. To investigate the effect of integrating the datasets from these three hospitals, we explored three combinations of datasets to increase the generalizability of our models. Specifically, three dataset options, namely, thyroid dysfunction group data from both Wakayama Medical University and Gunma University and control group data from Hidaka Hospital (referred to as Inst. comb. 1), thyroid dysfunction group data from Wakayama Medical University and control group data from Hidaka Hospital (referred to as Inst. comb. 2), and thyroid dysfunction group data from Gunma University and control group data from Hidaka Hospital (referred to as Inst. comb. 3), were set to train and evaluate the models.

Machine-learning algorithms

Four representative machine-learning algorithms were applied, and their thyroid dysfunction classification performance was evaluated:

The gradient boosting decision-tree (GBDT), as proposed by Friedman[18], produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees. The GBDT is based on a machine-learning technique that consists of an “ensemble” family of algorithms, creates multiple models (called weak learners), and combines them to increase prediction accuracy. The main idea of this technique is to build a set of decision trees and use them to classify a new case. Each decision-tree is generated using randomly selected variable subsets from all feature variables and a randomly selected subset of data combined by bootstrapping[19]. In this study, we employed the most accurate algorithm, called CATBoost[20], in the GBDT family.

The artificial neural network (ANN) is a well-established classification technique that is widely used in pattern recognition studies. In general, an ANN consists of 3 layers: an input layer that receives information, a hidden layer that processes information, and an output layer that calculates the results[21]. In the present study, a standard feed-forward ANN was applied due to its relative simplicity and stability.

The support vector machine (SVM) is a supervised machine-learning technique that is widely used in pattern recognition and classification problems. In this method, each data sample is a vector whose dimensions are equal to the number of features to be considered, and the SVM creates a hyperplane that separates samples into two categories. The induced hyperplane is constructed to maximize its distance from the samples of both classes. This algorithm achieves high classification performance by using special nonlinear functions called kernels to transform the input space into a multidimensional space[22]. In this study, the radial basis function kernel was used.

Logistic regression is a statistical classifier that provides the probability of predicting the labeled class of categorical type by using a number of attributes. Logistic regression is frequently used to examine the risk relationship between disease and exposure, with the ability to test for statistical interaction and control for multivariable confirmation[23]. Logistic regression is a linear model and is used as the baseline model for the performance comparison.

Explanatory features (variables) for machine learning

In terms of the input feature used in machine-learning models, we used the following 23 features referred to as Feature set 1, which are all features available at the four hospitals: sex, aspartate aminotransferase (AST), alanine aminotransferase (ALT), gamma-glutamyl transpeptidase (γ-GTP), red blood cell count (RBC), serum creatinine (S-Cr), alkaline phosphatase (ALP), uric acid (UA), lactic acid dehydrogenase (LDH), total protein (TP), blood urea nitrogen (BUN), albumin, albumin/globulin ratio (A/G), total cholesterol, total bilirubin (TB), C-reactivate protein, white blood count (WBC), hemoglobin (Hb), platelet, hematocrit, mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), and mean corpuscular hemoglobin concentration (MCHC). To further verify the performance of the model depending on the set of most basic laboratory tests for our aim of rapid screening for overlooked patients, we trained and evaluated a model limited to five routine tests referred to as Feature set 2, including AST, ALT, γ-GTP, total cholesterol, and sex, of which four features are the required (mandatory) laboratory tests conducted in the Japanese national health screening program called Specific Health Checkups.

Model validation

Cross-validation was applied to evaluate the performance of our machine-learning method in classifying patients. The evaluation was conducted by extracting 9/10 training data and 1/10 test data by conducting 10-fold cross-validation. This was repeated 10 times to extract the training and test data uniformly, and the average and standard deviation of each evaluation score of each time were calculated. During the model training and test process, we avoided including the same subject in both the training dataset and test dataset. The following measures were used for the performance evaluation criteria: the area under the receiver operating characteristic curve (AUROC); positive predictive value (PPV), defined as TP/(TP + FP); negative predictive value (NPV), defined as TN/(TN + FN); sensitivity, defined as TP/(TP + FN); and specificity, defined as TN/(TN + FP), where TP is the number of true positives, TN is the number of true negatives, FP is the number of false positives, and FN is the number of false negatives. Note that the cutoff value for classification as positive or negative is determined by the Youden[24] index.

In addition, the data of Kuma Hospital were employed for external validation. The model was constructed using the hyperthyroidism group and the hypothyroidism group of Wakayama Medical University and Gunma University and the control group of Hidaka Hospital as the training data. The model was evaluated using the hyperthyroidism group and hypothyroidism group of Kuma Hospital and the control group of Hidaka Hospital (referred to as External).

Feature importance

To further understand how each feature contributes to the classification of patients in our model, we introduced feature importance. Feature importance represents the factor by which the model error is increased compared to the original model error. In the decision-tree-based machine-learning algorithms, including the GBDT, impurities and the features at which the node is split are recorded for all the nodes when the decision-tree-learning process is finished, and the decision-tree calculates the feature importance using this information[19].

Table 3

Results of the validation of models with different labeling criteria, machine-learning algorithms, institutions, and input features.

No.		I	II	III	IV	V	VI	VII	VIII	IX
Training	Data labeling	Thyroid function test criterion	Prescription criterion	Thyroid function test criterion
	Institution combination	Inst. comb. 1	Inst. comb. 1	Inst. comb. 2	Inst. comb. 3	Inst. comb. 1
	Machine-learning algorithm	GBDT				SVM	Logistics regression	ANN	GBDT
	Input features	Feature set 1							Feature set 2	Feature set 1
Validation	Labeling criteria	Thyroid function test criterion
	Institution combination	Inst. comb. 1								External
Hyperthyroidism	AUROC	93.8 ± 2.7%	93.0 ± 2.3%	93.1 ± 2.4%	92.8 ± 3.0%	91.8 ± 3.4%	90.9 ± 3.3%	91.9 ± 2.7%	85.5 ± 3.9%	97.2 ± 0.5%
	PPV	80.3 ± 6.2%	81.4 ± 4.7%	76.6 ± 8.1%	78.2 ± 7.1%	71.6 ± 4.5%	79.4 ± 6.8%	73.9 ± 7.9%	72.4 ± 7.0%	98.5 ± 0.5%
	NPV	94.4 ± 2.7%	93.1 ± 3.4%	93.9 ± 2.7%	92.4 ± 3.5%	94.1 ± 3.7%	91.7 ± 3.7%	93.6 ± 3.4%	88.5 ± 4.2%	67.4 ± 6.0%
	Sensitivity	89.1 ± 5.8%	86.4 ± 7.7%	88.6 ± 5.5%	85.4 ± 7.0%	89.4 ± 6.7%	83.6 ± 8.4%	88.3 ± 7.0%	77.3 ± 9.7%	90.0 ± 2.9%
	Specificity	88.6 ± 4.7%	89.9 ± 3.5%	85.7 ± 6.3%	87.7 ± 5.1%	82.0 ± 4.1%	88.7 ± 4.6%	83.6 ± 6.7%	84.6 ± 5.9%	93.7 ± 2.1%
Hypothyroidism	AUROC	90.9 ± 3.3%	92.1 ± 3.2%	89.3 ± 2.2%	88.5 ± 4.5%	88.6 ± 4.0%	86.7 ± 3.1%	89.0 ± 3.6%	82.5 ± 3.7%	94.0 ± 1.5%
	PPV	79.9 ± 8.4%	73.9 ± 6.2%	73.2 ± 7.4%	72.9 ± 8.1%	74.1 ± 7.0%	67.7 ± 6.6%	71.6 ± 9.2%	70.0 ± 10.3%	59.8 ± 5.2%
	NPV	91.3 ± 5.3%	94.8 ± 3.8%	92.3 ± 4.7%	91.7 ± 3.6%	90.1 ± 2.3%	90.4 ± 4.3%	92.5 ± 2.9%	85.2 ± 2.5%	98.3 ± 0.8%
	Sensitivity	82.4 ± 12.5%	90.5 ± 7.2%	85.1 ± 10.2%	84.9 ± 6.7%	81.2 ± 4.7%	82.2 ± 9.2%	86.4 ± 6.3%	70.6 ± 8.2%	91.6 ± 3.9%
	Specificity	86.5 ± 6.8%	83.7 ± 5.2%	83.4 ± 7.9%	83.8 ± 6.2%	85.3 ± 5.3%	79.5 ± 7.7%	81.8 ± 8.0%	83.4 ± 8.6%	88.5 ± 2.6%

The mean and standard deviation for the tenfold cross-validation are shown for each score.

The evaluation metrics AUROC, PPV, NPV, sensitivity, and specificity for each model are shown. Two criteria for labeling of data, a thyroid test criterion and a prescription criterion, were devised. Inst. comb. 1 represents thyroid dysfunction group data from both Wakayama Medical University and Gunma University, and a control group data from Hidaka Hospital, Inst. comb. 2 represents thyroid dysfunction group data from Wakayama Medical University and a control group data from Hidaka Hospital, and Inst. comb. 3 represents thyroid dysfunction group data from Gunma University and a control group data from Hidaka Hospital. Feature set 1 is the full set of features available in the four hospitals, and Feature set 2 is limited to five routine tests that are mandatory for Japanese national special health check-ups. Four typical machine-learning algorithms for structured data, gradient boosting decision trees, support vector machines and neural networks used in related studies, as well as logistic regression, were examined.

Table 4

Evaluation results obtained without considering crosstalk.

No.		A-1	A-2
Training	Positive label criterion	Thyroid function test criterion
	Negative label setting	Crosstalk nonaccount
Validation	Positive label criterion	Thyroid function test criterion
	Negative label setting	Crosstalk nonaccount	Crosstalk account
Hyperthyroidism	AUROC	98.0 ± 2.2%	91.3 ± 2.3%
Hypothyroidism	AUROC	95.7 ± 3.1%	81.4 ± 4.4%

The mean and standard deviation for the tenfold cross-validation are shown for the AUROC scores. “Crosstalk account” represents the negative label setting, where both the control group and the hypothyroidism group were labeled negative in the hyperthyroidism group and both the control group and the hyperthyroidism group were labeled negative in the hypothyroidism group. “Crosstalk nonaccount” represents the negative label setting where only the control group was labeled negative.