ArticleFrontiers in endocrinology2022
Hypertension and Obesity: Risk Factors for Thyroid Disease.
Article in Frontiers in endocrinology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 18 citations in OpenAlex.
- Environmental exposure to mixtures of per- and polyfluoroalkyl substances in Northeast China: exploring links to nodular goiter and papillary thyroid carcinoma.Environmental health : a global access science source · 2025Article
- Global trend and disparity in the burden of thyroid cancer attributable to high body-mass index from 1990 to 2021 and projection to 2049: a systematic analysis based on the Global Burden of Disease Study 2021.BMC public health · 2025Article
- Association between total body muscle-fat ratio and risk of thyroid disorders: a cross-sectional study.BMC public health · 2024Article
- Cardiovascular and metabolic comorbidities in patients with thyroid nodules: the impact of incidental diagnosis.Journal of endocrinological investigation · 2024Article
- Novel Insights into Causal Effects of Lipid and Lipid-Lowering Targets with Autoimmune Thyroid Disease: A Mendelian Randomization Study.ImmunoTargets and therapy · 2024Article
- Navigating Thyroid Dysfunction and Comorbidities Among University Students in Abbottabad, Pakistan - A Cross-Sectional Evaluation of Screening Tool for Thyroid Dysfunction.International journal of general medicine · 2023Article
- Can Yoga Help to Manage the Symptoms of Thyroid Diseases?International journal of yogaReview
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Thyroid disease instances have rapidly increased in the past few decades; however, the cause of the disease remains unclear. Understanding the pathogenesis of thyroid disease will potentially reduce morbidity and mortality rates. Currently, the identified risk factors from existing studies are controversial as they were determined through qualitative analysis and were not further confirmed by quantitative implementations. Association rule mining, as a subset of data mining techniques, is dedicated to revealing underlying correlations among multiple attributes from a complex heterogeneous dataset, making it suitable for thyroid disease pathogenesis identification. This study adopts two association rule mining algorithms (i.e., Apriori and FP-Growth Tree) to identify risk factors correlated with thyroid disease. Extensive experiments were conducted to reach impartial findings with respect to knowledge discovery through two independent digital health datasets. The findings confirmed that gender, hypertension, and obesity are positively related to thyroid disease development. The history of I
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