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Synthesis and Regulation of Thyroid Hormones01:20

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Low blood levels of the thyroid hormones — triiodothyronine (T3) and thyroxine (T4) — signal the hypothalamus to release the thyrotropin-releasing hormone (TRH). TRH then reaches the pituitary gland and stimulates the release of thyroid-stimulating hormone(TSH) into the bloodstream.
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The Thyroid Gland01:23

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The thyroid gland is a small, butterfly-shaped gland located in the neck and covers the anterior surface of the trachea. The gland has two lateral lobes connected by a thin tissue mass called the isthmus. Internally, each lobe comprises many small spherical structures known as thyroid follicles, surrounded by a network of blood vessels.
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The thyroid hormone (TH) plays a pivotal role in the intricate orchestration of physiological processes, exerting profound effects on development, metabolism, and homeostasis throughout different life stages.
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改进的生物灵感与机器学习计算方法用于甲状腺预测.

Divya Kesavulu1, Kannadasan R2

  • 1School of Computer Science and Engineering, Vellore Institute of Technology (VIT), Vellore, 632014, India.

Scientific reports
|July 2, 2025
PubMed
概括

通过粒子蛇群优化 (PSSO) 增强的机器学习模型显著提高了甲状腺疾病预测的准确性. 普索-随机森林模型实现了98.7%的准确性,超过了深度学习方法.

关键词:
美国有线电视新闻网 (CNN-LSTM)DT DT DT DT DT DT DT DT DT DT DTD DTD DTD DTD DTD DTD DTD DTJ DTJ DTJ DTJ DTJ DTJ DTJ DTJ DTJ DTJ DTJ DTJ DTJ深度学习是一种深度学习.选择功能选择功能选择.在 KNN KNN 标签上.机器学习是机器学习.优化技术的优化技术粒子蛇群群的优化 粒子蛇群的优化这就是为什么RF是RF,RF是RF在SVM中,SVM是SVM.甲状腺是什么?甲状腺是什么?

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科学领域:

  • 医疗信息学 医疗信息学
  • 计算生物学 计算生物学
  • 医疗保健中的人工智能

背景情况:

  • 甲状腺疾病,包括甲状腺功能低下症和甲状腺功能过高症,是全球普遍存在的健康问题,具有显著的代谢和福祉影响,特别影响亚洲,拉丁美洲和非洲的妇女.
  • 准确及时诊断甲状腺疾病对于有效的患者管理和预防相关的健康并发症至关重要.

研究的目的:

  • 研究各种机器学习 (ML) 和深度学习 (DL) 模型的有效性,以提高甲状腺疾病预测的精度.
  • 为了优化ML模型的性能,使用高级技术,如粒子蛇群优化 (PSSO).

主要方法:

  • 评估多种机器学习算法,包括随机森林 (RF),决策树,支持矢量机 (SVM) 和K-最近邻居 (KNN).
  • 应用粒子蛇群优化 (PSSO) 来增强选择的ML模型的预测能力.
  • 使用关键指标进行绩效评估:准确性,回忆力,精度,F1分数和特异性.

主要成果:

  • 使用PSSO (PSSO-RF) 优化的随机森林模型表现出卓越的预测性能,达到98.7%的准确性.
  • PSSO-RF显著超过了CNN-LSTM深度学习基线,精度提高了2.98% (98.7%与95.72%相比).
  • 优化的模型在所有评估指标上取得了高分:98.47%的F1分数,98.51%的精度,98.7%的回忆率和98%的特异性.

结论:

  • 生物灵感优化技术,如PSSO,可以显著提高疾病预测的传统机器学习模型的性能.
  • PSSO-RF模型代表了一种高效的计算方法,用于准确检测甲状腺疾病,超越了当前最先进的方法.
  • 这项研究强调了将先进的计算创新集成到医疗保健中的潜力,以提高诊断准确性和患者的结果.