AIMC Topic: Thyroid Diseases
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Thyroid disease classification using generative adversarial networks and Kolmogorov-Arnold network for three-class classification.
BMC medical informatics and decision making
Jul 31, 2025
Thyroid disease classification is a critical challenge in medical diagnostics, requiring accurate differentiation between hyperthyroidism, hypothyroidism, and normal thyroid function. This study introduces an advanced machine learning approach that i...
Improved bio-inspired with machine learning computing approach for thyroid prediction.
Scientific reports
Jul 2, 2025
Thyroid illness is widely recognised as a prevalent health condition that can result in a range of health disorders. Thyroid illnesses, namely hypothyroidism and hyperthyroidism, are widespread worldwide and present considerable health consequences. ...
Comprehensive framework for thyroid disorder diagnosis: Integrating advanced feature selection, genetic algorithms, and machine learning for enhanced accuracy and other performance matrices.
PloS one
Jun 18, 2025
Thyroid hormones control crucial physiological activities, such as metabolism, oxidative stress, erythropoiesis, thermoregulation, and organ development. Hormonal imbalances may cause serious conditions like cognitive impairment, depression, and nerv...
Leveraging machine learning in precision medicine to unveil organochlorine pesticides as predictive biomarkers for thyroid dysfunction.
Scientific reports
Apr 11, 2025
Exposure to organochlorine pesticides (OCPs) poses significant health risks, including cancer, endocrine dysregulation, neurological disorders, and reproductive disruption. This study investigates the association between OCP exposure and thyroid dist...
Web-Based Explainable Machine Learning-Based Drug Surveillance for Predicting Sunitinib- and Sorafenib-Associated Thyroid Dysfunction: Model Development and Validation Study.
JMIR formative research
Apr 10, 2025
BACKGROUND: Unlike one-snap data collection methods that only identify high-risk patients, machine learning models using time-series data can predict adverse events and aid in the timely management of cancer.
Machine learning in lymphocyte and immune biomarker analysis for childhood thyroid diseases in China.
BMC pediatrics
Mar 28, 2025
OBJECTIVE: This study aims to characterize and analyze the expression of representative biomarkers like lymphocytes and immune subsets in children with thyroid disorders. It also intends to develop and evaluate a machine learning model to predict if ...
Enhanced interpretable thyroid disease diagnosis by leveraging synthetic oversampling and machine learning models.
BMC medical informatics and decision making
Nov 29, 2024
Thyroid illness encompasses a range of disorders affecting the thyroid gland, leading to either hyperthyroidism or hypothyroidism, which can significantly impact metabolism and overall health. Hypothyroidism can cause a slowdown in bodily processes, ...
A novel meta learning based stacked approach for diagnosis of thyroid syndrome.
PloS one
Nov 1, 2024
Thyroid syndrome, a complex endocrine disorder, involves the dysregulation of the thyroid gland, impacting vital physiological functions. Common causes include autoimmune disorders, iodine deficiency, and genetic predispositions. The effects of thyro...
Analysis and interpretability of machine learning models to classify thyroid disease.
PloS one
May 31, 2024
Thyroid disease classification plays a crucial role in early diagnosis and effective treatment of thyroid disorders. Machine learning (ML) techniques have demonstrated remarkable potential in this domain, offering accurate and efficient diagnostic to...
[Involvement of essential trace elements in the pathogenesis of thyroid diseases: diagnostic markers and analytical methods for determination].
Problemy endokrinologii
Apr 2, 2024
AIM: To study the role of iodine, selenium and zinc in the pathogenesis of iodine deficiency and autoimmune thyroid diseases and scientifically substantiate the choice of security biomarkers and analytical methods for determination.
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