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Low blood levels of the thyroid hormones — triiodothyronine (T3) and thyroxine (T4) — signal the hypothalamus to release the thyrotropin-releasing hormone (TRH). TRH then reaches the pituitary gland and stimulates the release of thyroid-stimulating hormone(TSH) into the bloodstream.
Upon reaching the thyroid gland, TSH stimulates the follicular cells' active uptake of iodide ions from the blood. The ions diffuse to the apical surface of the cells and are oxidized to iodine. The...
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Web-Based Application for Hashimoto's Disease Prediction Based on Thyroid Hormone Levels and Machine Learning

Kypros Andreou1, Panagiotis Vlamos2, Marios G Krokidis2

  • 1Department of Informatics, Ionian University, Corfu, Greece. kyprosantreou@outlook.com.

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Summary

This study developed a machine learning tool to detect Hashimoto

Keywords:
BiomarkersHashimoto’s diseaseHashimoto’s thyroiditisRandom forestWeb application

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Area of Science:

  • Endocrinology
  • Immunology
  • Computational Biology

Background:

  • Hashimoto's thyroiditis is an autoimmune disorder affecting the thyroid gland.
  • Diagnosis relies on clinical symptoms, anti-thyroid antibodies, and histology.
  • Pathophysiology involves thyroid enlargement, lymphocytic infiltration, and specific antibodies.

Purpose of the Study:

  • To identify biomarkers for Hashimoto's thyroiditis.
  • To develop a web-based application for disease detection using machine learning.
  • To provide a practical tool for early diagnosis and management.

Main Methods:

  • Collected relevant biomarkers for Hashimoto's thyroiditis.
  • Developed a web application using Python.
  • Utilized the Random Forest algorithm for machine learning model training.

Main Results:

  • The machine learning model was trained on an existing dataset.
  • The web application enables prediction of Hashimoto's thyroiditis based on blood tests.
  • The tool offers a practical approach for early disease detection.

Conclusions:

  • The developed web application is a valuable tool for early diagnosis and management of Hashimoto's thyroiditis.
  • Machine learning algorithms can aid in identifying individuals with the condition.
  • Further research is needed to address limitations and enhance the tool's capabilities.