Prediction of malignant transformation in oral epithelial dysplasia using machine learning.
- Ingham, James
- Smith, Caroline I
- Ellis, Barnaby G
- Whitley, Conor A
- Triantafyllou, Asterios
- Gunning, Philip J
- Barrett, Steve D
- Gardener, Peter
- Shaw, Richard J
- Risk, Janet M
- Weightman, Peter
DOIAbstract
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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