Identification of Important Symptoms and Diagnostic Hypothyroidism Patients Using Machine Learning Algorithms

Hypothyroidism (HT) is one of the most common endocrine diseases. It is, however, usually challenging for physicians to diagnose due to non-specific symptoms. The usual procedure for diagnosis of HT is a blood test. In recent years, machine learning algorithms have proved to be powerful tools in medicine due to their diagnostic accuracy. In this study, we aim to predict and identify the most important symptoms of HT using machine learning algorithms.

Study Overview

• Status: Completed
• Conditions: Prediction Hypothyroidism Patients Using Machine Learning Algorithms; Identification of Important Symptoms of Hypothyroidism
• Intervention / Treatment: Other: There was no intervention in this study

Detailed Description

Hypothyroidism (HT) is one of the most common diseases in the world, in which insufficient thyroid hormone is produced. Due to the wide variation in clinical symptoms, the definition of HT is mainly biochemical. Ninety nine percent of primary cases of HT are related to deficiency of thyroxine (T4) and triiodothyronine (T3) hormones. Deficiency in T4 and T3 hormones, which are produced by thyroid gland, leads to increasing thyroid-stimulating hormone (TSH) production through a negative feedback mechanism .

HT has non-specific symptoms such as weight gain, fatigue, insufficient concentration, depression, menstrual irregularities, and constipation, which change with age, gender, and other factors. Autoimmune thyroiditis (Hashimoto's disease) is the most common symptom of this disorder.

The prevalence of HT is 2% in the world, even in the existence of enough iodine in daily food. In a cohort study that was conducted in Iran in 2017, a significant increase in the prevalence of thyroid dysfunction was reported, from 1.4 to 10.5, attributed to several factors such as geographical areas, aging, ethnicity and the amount of iodine intake.

Increasing in serum cholesterol levels and the risk of coronary artery disease and cardiovascular mortality are the most common complications of HT. The economic burden of HT is fairly high, especially in patients with other underlying diseases such as diabetes and hemodialysis. The common clinical method for diagnosing equally primary HT is to check the serum concentration of TSH; People with TSH and T4 levels above the reference age range are diagnosed as hypothyroid. The upper limit of the TSH reference range usually increases with age in adults .

In recent years, artificial intelligence and machine learning techniques have attracted increasing attention from medical researchers. Among the most attractive features of machine learning in medicine are disease prediction and diagnosis of simple symptoms . The prediction models such as support vector machine (SVM), decision tree (DT), random forest (RF) and artificial neural network (ANN), are among the most popular machine learning methods.

As accurate diagnostic of HT is currently based on the TSH level obtained by a blood test, it creates some expense burden and anxiety for patients. The aim of the present study is to first diagnose HT in new cases that have no history of HT symptoms with three statistical machine learning methods (logistic regression, decision tree and random forest). The diagnosis is performed using simple and widely-accepted visual symptoms of HT that endocrinologists identify. Second, the most important visual features of HT which can help physicians in diagnosis, are also ranked using decision tree and random forest methods.

• Study Type: Observational
• Enrollment (Actual): 1296

Contacts and Locations

Study Locations

• Iran, Islamic Republic of
  • Kerman, Iran, Islamic Republic of, 7616913555
    • Faculty of Health, Kerman University of Medical Sciences

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

• Sampling Method: Non-Probability Sample

Study Population

In total 1296 individuals (1088 women and 208 men) aged 18 years or over participated in this cross-sectional study from September to December 2022 at our main clinic for thyroid treatment.

Description

Inclusion Criteria:

• Clinical diagnosis of Hypothyroidism Disease
• aged 18 years or more

Exclusion Criteria:

• Having history of Hypothyroidism treatment and thyroid gland surgery
• Having HT during previous pregnancies

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
with Hypothyroidism, without Hypothyroidism	Other: There was no intervention in this study; There was no intervention in this study

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
physiological parameter	Information about hypothyroidism was collected by checklist. Then, TSH test was used for each individual to obtain the response variable. People whose TSH level is above 4 mIU/L are identified as hypothyroid. A person whose TSH is between 0.4 and 0.4 mIU/L is considered healthy.	6 months

Collaborators and Investigators

• Sponsor: Kerman University of Medical Sciences

Study record dates

Study Major Dates

• Study Start (Actual): September 12, 2022
• Primary Completion (Actual): September 12, 2022
• Study Completion (Actual): September 20, 2023

Study Registration Dates

• First Submitted: October 25, 2023
• First Submitted That Met QC Criteria: October 30, 2023
• First Posted (Actual): November 2, 2023

Study Record Updates

• Last Update Posted (Actual): November 2, 2023
• Last Update Submitted That Met QC Criteria: October 30, 2023
• Last Verified: October 1, 2023

More Information

Terms related to this study

• Keywords: Hypothyroidism; Decision tree; Logistic regression; Machine learning algorithms; Random Forest
• Additional Relevant MeSH Terms: Endocrine System Diseases; Thyroid Diseases; Hypothyroidism
• Other Study ID Numbers: 401000292

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Plan Description: The data is related to the common symptoms of hypothyroidism. Also, this data includes 6 demographic variables. If a researcher conducts research on hypothyroidism and machine learning, he/she can access the data by citing sufficient reasons.
• IPD Sharing Time Frame: As soon as satisfactory confirmation is given, the data will be sent. This may take between one and two weeks
• IPD Sharing Access Criteria: Any research related to hypothyroidism and its diagnosis methods using simple symptoms. Use in the field of machine learning
• IPD Sharing Supporting Information Type: STUDY_PROTOCOL; SAP; ICF

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No