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- W1559781606 abstract "In medicinal diagnosis, it is desirable to estimate a patient’s risk of disease. Herein is reported a probabilistic strategy of discriminant partial least squares coupled with near-infrared spectroscopy for the diagnosis of colorectal cancer. A total of 288 spectra from twenty-three patients with a partial colorectal resection were collected and partitioned into training and test sets. Principal component analysis was used for preliminary analysis. For each sample, the discriminant partial least squares model produced an output that was not a probability. Platt scaling was used to fit a posterior probability by a sigmoidal function, in which the optimization parameters were determined by regularized maximum likelihood. Only five and six spectra were misclassified for the training and the test sets, respectively. The spectral diagnosis of tissue was accompanied by a probability that represented the confidence of the classification. The combination of near-infrared spectroscopy and a probabilistic output mo..." @default.
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- W1559781606 date "2015-06-08" @default.
- W1559781606 modified "2023-09-24" @default.
- W1559781606 title "Probabilistic Characterization Using Partial Least Squares and Near-Infrared Spectroscopy for Cancer Diagnosis" @default.
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- W1559781606 doi "https://doi.org/10.1080/00032719.2015.1027900" @default.
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