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

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.