James Ingham, Caroline I Smith, Barnaby G Ellis, Conor A Whitley, Asterios Triantafyllou, Philip J Gunning, Steve D Barrett, Peter Gardener, Richard J Shaw, Janet M Risk, Peter Weightman
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引用次数: 0
摘要
一种机器学习算法（MLA）被应用于傅立叶变换红外光谱（FTIR）数据集，该数据集之前曾用主成分分析（PCA）线性判别分析（LDA）模型进行过分析。这一比较证实了傅立叶变换红外光谱作为口腔上皮发育不良（OED）预后工具的稳健性。MLA 预测恶性肿瘤的灵敏度为 84 ± 3%，特异度为 79 ± 3%。它提供的关键波数对于开发可用于改善 OED 预后的设备非常重要。
Prediction of malignant transformation in oral epithelial dysplasia using machine learning.
A machine learning algorithm (MLA) has been applied to a Fourier transform infrared spectroscopy (FTIR) dataset previously analysed with a principal component analysis (PCA) linear discriminant analysis (LDA) model. This comparison has confirmed the robustness of FTIR as a prognostic tool for oral epithelial dysplasia (OED). The MLA is able to predict malignancy with a sensitivity of 84 ± 3% and a specificity of 79 ± 3%. It provides key wavenumbers that will be important for the development of devices that can be used for improved prognosis of OED.
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