Use of Raman spectroscopy to screen diabetes mellitus with machine learning tools
Autor: | Claudia Luevano-Contreras, Juan Carlos Torres-Galván, Francisco Javier González, Miguel G. Ramírez-Elías, Edgar Guevara |
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Rok vydání: | 2018 |
Předmět: |
0301 basic medicine
Screen-diabetes mellitus Computer science Machine vision Machine learning computer.software_genre 01 natural sciences Article Enzymatic Assays 010309 optics symbols.namesake 03 medical and health sciences 0103 physical sciences Spectral data 030304 developmental biology 0303 health sciences Artificial neural network business.industry 010401 analytical chemistry Type 2 Diabetes Mellitus Plasma glucose concentration Atomic and Molecular Physics and Optics 0104 chemical sciences Support vector machine 030104 developmental biology Principal component analysis symbols Artificial intelligence Raman spectroscopy business computer Biotechnology |
Zdroj: | Biomedical Optics Express. 9:4998 |
ISSN: | 2156-7085 |
Popis: | Type 2 diabetes mellitus (DM2) is one of the most widely prevalent diseases worldwide and is currently screened by invasive techniques based on enzymatic assays that measure plasma glucose concentration in a laboratory setting. A promising plan of action for screening DM2 is to identify molecular signatures in a non-invasive fashion. This work describes the application of portable Raman spectroscopy coupled with several supervised machine-learning techniques, to discern between diabetic patients and healthy controls (Ctrl), with a high degree of accuracy. Using artificial neural networks (ANN), we accurately discriminated between DM2 and Ctrl groups with 88.9–90.9% accuracy, depending on the sampling site. In order to compare the ANN performance to more traditional methods used in spectroscopy, principal component analysis (PCA) was carried out. A subset of features from PCA was used to generate a support vector machine (SVM) model, albeit with decreased accuracy (76.0–82.5%). The 10-fold cross-validation model was performed to validate both classifiers. This technique is relatively low-cost, harmless, simple and comfortable for the patient, yielding rapid diagnosis. Furthermore, the performance of the ANN-based method was better than the typical performance of the invasive measurement of capillary blood glucose. These characteristics make our method a promising screening tool for identifying DM2 in a non-invasive and automated fashion. |
Databáze: | OpenAIRE |
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