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基于ATR-FTIR频谱特征选择和疾病诊断的相互信息矩阵的PAM聚类算法

Francesca Condino¹, Maria Caterina Crocco^2,3,4, Rita Guzzi^2,4

¹Department of Economics, Statistics and Finance "Giovanni Anania", University of Calabria, Rende (CS), 87036, Italy. francesca.condino@unical.it.

BMC medical research methodology

|October 2, 2025

概括

这项研究引入了一种使用Medoids周围分区来从ATR-FTIR数据中选择疾病歧视的光谱生物标志物的新方法. 这种方法有效地识别了用于预测多发性硬化症的信息波数.

科学领域:

生物医学光谱学生物医学光谱学
在医疗保健中的数据科学.
神经系统疾病诊断神经系统疾病诊断

背景情况:

减弱总反射度里埃变换红外光谱 (ATR-FTIR) 为诊断各种病理提供了宝贵的数据,包括神经系统疾病.
从大型数据集中选择有信息的光谱生物标记因数据冗余而具有挑战性,阻碍了疾病的准确歧视.

研究的目的:

开发一种新的特征选择方法,用于从ATR-FTIR数据中识别光谱生物标志物.
利用光谱数据中的冗余信息来改进疾病预测和解释.

主要方法:

采用了一种基于分区围绕Medoids (PAM) 算法的新特征选择方法.
从相互信息中获得的不相似性矩阵被用来组合具有相似依赖模式的波数.
每个集群的中体,代表观察到的数据点,被选为潜在的光谱生物标志物.

主要成果:

提出的方法成功地根据它们的对依赖模式将波数分组在一起.
从这些星团中得出的 medoids 被发现是光谱数据的代表.
该方法在使用真实数据来区分多发性硬化症患者和健康受试者方面表现出有效性.

结论:

围绕Medoids的分区算法,利用相互的信息,为光谱生物标志物发现提供了一种可解释的方法.
这种方法有助于选择强大的光谱特征用于神经系统疾病的疾病预测.
该研究强调了ATR-FTIR光谱数据和临床诊断的高级特征选择的潜力.

关键词:

集群集成是指集群集成.依赖关系依赖关系不相似性衡量方法不相似性衡量方法功能选择功能选择香农 Entropy 香农的是什么意思

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