Web-Based Application for Hashimoto's Disease Prediction Based on Thyroid Hormone Levels and Machine Learning Analysis.

Kypros Andreou, Panagiotis Vlamos, Marios G Krokidis

Abstract read

In one paragraph

Article in Advances in experimental medicine and biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Kypros AndreouDepartment of Informatics, Ionian University, Corfu, Greece. kyprosantreou@outlook.com.

Panagiotis VlamosDepartment of Informatics, Ionian University, Corfu, Greece.

Marios G KrokidisDepartment of Informatics, Ionian University, Corfu, Greece.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hashimoto's disease or Hashimoto's thyroiditis is an autoimmune disorder of the thyroid gland. The pathophysiology of the disorder is characterized by an increased volume of the thyroid gland, infiltration of the parenchyma by lymphocytes, and the presence of antibodies specific for thyroid antigens. The diagnosis of Hashimoto thyroiditis is based on the presence of clinical symptoms, anti-thyroid antibodies, and certain histologic features. The present study aimed to collect biomarkers and create a web-based application for detecting the disease through machine learning. The web application was created using Python and Random Forest algorithm was selected to train the existing dataset. Taking advantage of machine learning algorithms, the application provides users with a practical means for predicting immortality based on their blood tests. Although there are limitations that need to be addressed, representing a valuable tool for early diagnosis and management of the disease is required.

Indexed as

Hashimoto DiseaseInternetMachine LearningThyroid HormonesAlgorithmsAutoantibodiesBiomarkersHumansAutoantibodiesBiomarkersThyroid HormonesBiomarkersHashimoto’s diseaseHashimoto’s thyroiditisRandom forestWeb application

Identifiers

PMID41479087

DOI10.1016/j.autrev.2020.102529

What OpenQuestion holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.