提高使用分散反射光谱和回归模型的甲状腺功能诊断评估
W Anto Win Shalini1, T Rajalakshmi1, S Vasanthadev Suryakala1
1Department of Electronics and Communication Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, India.
Journal of biophotonics
|March 21, 2025
概括
扩散反射光谱 (DRS) 显示出对非侵入性甲状腺功能障碍查的希望. 部分最小平方回归 (PLSR) 模型与标准正常变量 (SNV) 预处理在诊断甲状腺疾病方面取得了高准确性.
科学领域:
- 生物医学光学 生物医学光学
- 频谱学是一种光谱学.
- 医学诊断 医学诊断 医学诊断
背景情况:
- 甲状腺功能障碍是一个广泛的健康问题,需要早期检测.
- 非侵入性查方法对于及时诊断和管理至关重要.
- 扩散反射光谱 (DRS) 提供了一个潜在的非接触方法.
研究的目的:
- 调查扩散反射光谱 (DRS) 对于诊断甲状腺功能障碍的有效性.
- 评估各种光谱预处理技术和预测模型.
- 为了确定最优的模型,准确地检测甲状腺功能障碍.
主要方法:
- 收集了31名甲状腺功能障碍患者的光谱数据.
- 应用的预处理技术:标准正常变量 (SNV),乘法分散校正 (MSC) 和基线校正.
- 使用的回归模型:PLSR,PCR,LASSO,随机森林,,GPR和贝叶斯回归.
主要成果:
- 部分最小平方回归 (PLSR) 模型与SNV预处理相结合,证明了卓越的性能.
- 获得了高的确定系数 (R2 = 0.93) 和低的预测误差 (RMSE = 0.29,MSE = 0.08).
- 皮尔森的二次测试证实了适合开发模型的良性.
结论:
- 与适当的预处理和PLSR建模相结合的DRS是非侵入性甲状腺功能障碍诊断的有效方法.
- 该SNV预处理技术显著提高了PLSR模型的预测准确度.
- 这种方法具有早期和可访问的甲状腺健康查的潜力.
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