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

NCT06112886 | Completed

• 1 other identifier: 401000292
• Study Type: observational
• Target: 1,296
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Prediction Hypothyroidism Patients Using Machine Learning Algorithms; Identification of Important Symptoms of Hypothyroidism

Keywords

Hypothyroidism; Machine learning algorithms; Random Forest; Decision tree; Logistic regression

Outcome Measures

Primary Outcomes (1)

• 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

Study Arms (1)

with Hypothyroidism, without Hypothyroidism

Other: There was no intervention in this study

Interventions

There was no intervention in this study OTHER

There was no intervention in this study

with Hypothyroidism, without Hypothyroidism

Eligibility Criteria

• Age: 18 Years+
• Gender Eligibility Details: The sexual identity of people is based on their physiological appearance
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

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

You may not qualify if:

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

Sponsors & Collaborators

• Kerman University of Medical Sciences: lead

Study Sites (1)

Faculty of Health, Kerman University of Medical Sciences

Kerman, 7616913555, Iran

Location

MeSH Terms

Conditions

Hypothyroidism

Condition Hierarchy (Ancestors)

Thyroid Diseases; Endocrine System Diseases

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Study Record Dates

• First Submitted: October 25, 2023
• First Posted: November 2, 2023
• Study Start: September 12, 2022
• Primary Completion: September 12, 2022
• Study Completion: September 20, 2023
• Last Updated: November 2, 2023

Record last verified: 2023-10

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, SAP, ICF
• Time Frame: As soon as satisfactory confirmation is given, the data will be sent. This may take between one and two weeks
• Access Criteria: Any research related to hypothyroidism and its diagnosis methods using simple symptoms. Use in the field of machine learning

Locations

IR (1)