Literature DB >> 31565504

Use of Raman spectroscopy to screen diabetes mellitus with machine learning tools: comment.

Ivan A Bratchenko1, Dmitry N Artemyev1, Yulia A Khristoforova1, Lyudmila A Bratchenko1.   

Abstract

This paper comments on the article "Use of Raman spectroscopy to screen diabetes mellitus with machine learning tools" by E. Guevara et al. The authors propose an optical method for noninvasive automated screening of type 2 diabetes mellitus. Despite the high performance of the proposed method, results shown by the authors may be ambiguous due to the overestimation of classification models for Raman spectral data analysis.
© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.

Entities:  

Year:  2019        PMID: 31565504      PMCID: PMC6757474          DOI: 10.1364/BOE.10.004489

Source DB:  PubMed          Journal:  Biomed Opt Express        ISSN: 2156-7085            Impact factor:   3.732


  5 in total

1.  Learning capability and storage capacity of two-hidden-layer feedforward networks.

Authors:  Guang-Bin Huang
Journal:  IEEE Trans Neural Netw       Date:  2003

2.  Artificial neural networks for small dataset analysis.

Authors:  Antonello Pasini
Journal:  J Thorac Dis       Date:  2015-05       Impact factor: 2.895

3.  Use of Raman spectroscopy to screen diabetes mellitus with machine learning tools.

Authors:  Edgar Guevara; Juan Carlos Torres-Galván; Miguel G Ramírez-Elías; Claudia Luevano-Contreras; Francisco Javier González
Journal:  Biomed Opt Express       Date:  2018-09-26       Impact factor: 3.732

4.  Raman spectroscopy provides a powerful diagnostic tool for accurate determination of albumin glycation.

Authors:  Narahara Chari Dingari; Gary L Horowitz; Jeon Woong Kang; Ramachandra R Dasari; Ishan Barman
Journal:  PLoS One       Date:  2012-02-29       Impact factor: 3.240

Review 5.  Advances in the in Vivo Raman Spectroscopy of Malignant Skin Tumors Using Portable Instrumentation.

Authors:  Nikolaos Kourkoumelis; Ioannis Balatsoukas; Violetta Moulia; Aspasia Elka; Georgios Gaitanis; Ioannis D Bassukas
Journal:  Int J Mol Sci       Date:  2015-06-26       Impact factor: 5.923

  5 in total

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