THYROID DISORDER PREDICTION USING SPECTROSCOPY BASED ON MACHINE LEARNING

Thyroid disorders are among the most common endocrine disorders, affecting millions of people worldwide. Early detection of thyroid disorders is essential for effective treatment and management of the condition. Currently, laboratory tests are used to diagnose thyroid disorders, but these tests can be time-consuming and expensive. In recent years, there has been growing interest in the use of spectroscopy and machine learning for the prediction of thyroid disorders.
Spectroscopy is a powerful tool for the analysis of biological samples. It involves the measurement of the interaction between light and matter, allowing for the identification and quantification of various chemical compounds. Spectroscopy can be used to detect changes in the biochemical composition of biological tissues, which can be indicative of thyroid disorders.
Machine learning is a subfield of artificial intelligence that involves the development of algorithms that can learn from data. Machine learning algorithms can be trained to recognize patterns in spectroscopic data that are associated with thyroid disorders. Once trained, these algorithms can be used to predict the likelihood of a patient having a thyroid disorder based on their spectroscopic data.
Several studies have investigated the use of spectroscopy and machine learning for the prediction of thyroid disorders. One study used near-infrared (NIR) spectroscopy to analyze the thyroid gland of patients with thyroid disorders. The study found that NIR spectroscopy could accurately distinguish between healthy thyroid tissue and tissue with thyroid disorders.
Another study used Raman spectroscopy to analyze the blood of patients with thyroid disorders. The study found that Raman spectroscopy could accurately distinguish between healthy blood samples and blood samples from patients with thyroid disorders. The study also found that machine learning algorithms could be used to predict the presence of thyroid disorders based on Raman spectroscopic data.