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

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References

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  1. E. Guevara, J. C. Torres-Galván, M. G. Ramírez-Elías, C. Luevano-Contreras, and F. J. González, “Use of Raman spectroscopy to screen diabetes mellitus with machine learning tools,” Biomed. Opt. Express 9(10), 4998–5010 (2018).
    [Crossref] [PubMed]
  2. N. Kourkoumelis, I. Balatsoukas, V. Moulia, A. Elka, G. Gaitanis, and I. D. Bassukas, “Advances in the in Vivo Raman Spectroscopy of Malignant Skin Tumors Using Portable Instrumentation,” Int. J. Mol. Sci. 16(7), 14554–14570 (2015).
    [Crossref] [PubMed]
  3. I.N. da Silva, “Artificial Neural Networks, A Practical Course,” (Springer 2017).
  4. C. Cortes and V. Vapnik, “Support-vector networks,” Mach. Learn. 20(3), 273–297 (1995).
    [Crossref]
  5. G. Hughes, “On the mean accuracy of statistical pattern recognizers,” IEEE Trans. Inf. Theory 14(1), 55–63 (1968).
    [Crossref]
  6. G.-B. Huang, “Learning capability and storage capacity of two-hidden-layer feedforward networks,” IEEE Trans. Neural Netw. 14(2), 274–281 (2003).
    [Crossref] [PubMed]
  7. A. Pasini, “Artificial neural networks for small dataset analysis,” J. Thorac. Dis. 7(5), 953–960 (2015).
    [PubMed]
  8. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2nd ed., Wiley (2001).
  9. S. Guo, T. Bocklitz, U. Neugebauer, and J. Popp, “Common Mistakes in Cross-Validating Classification Models,” Anal. Methods 9(30), 4410–4417 (2017).
    [Crossref]
  10. N. C. Dingari, G. L. Horowitz, J. W. Kang, R. R. Dasari, and I. Barman, “Raman spectroscopy provides a powerful diagnostic tool for accurate determination of albumin glycation,” PLoS One 7(2), e32406 (2012).
    [Crossref] [PubMed]
  11. T. Hastie, R. Tibshirani, and J. Friedman, “The Elements of Statistical Learning. Data Mining, Inference, and Prediction”, 2nd edition (2008).

2018 (1)

2017 (1)

S. Guo, T. Bocklitz, U. Neugebauer, and J. Popp, “Common Mistakes in Cross-Validating Classification Models,” Anal. Methods 9(30), 4410–4417 (2017).
[Crossref]

2015 (2)

N. Kourkoumelis, I. Balatsoukas, V. Moulia, A. Elka, G. Gaitanis, and I. D. Bassukas, “Advances in the in Vivo Raman Spectroscopy of Malignant Skin Tumors Using Portable Instrumentation,” Int. J. Mol. Sci. 16(7), 14554–14570 (2015).
[Crossref] [PubMed]

A. Pasini, “Artificial neural networks for small dataset analysis,” J. Thorac. Dis. 7(5), 953–960 (2015).
[PubMed]

2012 (1)

N. C. Dingari, G. L. Horowitz, J. W. Kang, R. R. Dasari, and I. Barman, “Raman spectroscopy provides a powerful diagnostic tool for accurate determination of albumin glycation,” PLoS One 7(2), e32406 (2012).
[Crossref] [PubMed]

2003 (1)

G.-B. Huang, “Learning capability and storage capacity of two-hidden-layer feedforward networks,” IEEE Trans. Neural Netw. 14(2), 274–281 (2003).
[Crossref] [PubMed]

1995 (1)

C. Cortes and V. Vapnik, “Support-vector networks,” Mach. Learn. 20(3), 273–297 (1995).
[Crossref]

1968 (1)

G. Hughes, “On the mean accuracy of statistical pattern recognizers,” IEEE Trans. Inf. Theory 14(1), 55–63 (1968).
[Crossref]

Balatsoukas, I.

N. Kourkoumelis, I. Balatsoukas, V. Moulia, A. Elka, G. Gaitanis, and I. D. Bassukas, “Advances in the in Vivo Raman Spectroscopy of Malignant Skin Tumors Using Portable Instrumentation,” Int. J. Mol. Sci. 16(7), 14554–14570 (2015).
[Crossref] [PubMed]

Barman, I.

N. C. Dingari, G. L. Horowitz, J. W. Kang, R. R. Dasari, and I. Barman, “Raman spectroscopy provides a powerful diagnostic tool for accurate determination of albumin glycation,” PLoS One 7(2), e32406 (2012).
[Crossref] [PubMed]

Bassukas, I. D.

N. Kourkoumelis, I. Balatsoukas, V. Moulia, A. Elka, G. Gaitanis, and I. D. Bassukas, “Advances in the in Vivo Raman Spectroscopy of Malignant Skin Tumors Using Portable Instrumentation,” Int. J. Mol. Sci. 16(7), 14554–14570 (2015).
[Crossref] [PubMed]

Bocklitz, T.

S. Guo, T. Bocklitz, U. Neugebauer, and J. Popp, “Common Mistakes in Cross-Validating Classification Models,” Anal. Methods 9(30), 4410–4417 (2017).
[Crossref]

Cortes, C.

C. Cortes and V. Vapnik, “Support-vector networks,” Mach. Learn. 20(3), 273–297 (1995).
[Crossref]

Dasari, R. R.

N. C. Dingari, G. L. Horowitz, J. W. Kang, R. R. Dasari, and I. Barman, “Raman spectroscopy provides a powerful diagnostic tool for accurate determination of albumin glycation,” PLoS One 7(2), e32406 (2012).
[Crossref] [PubMed]

Dingari, N. C.

N. C. Dingari, G. L. Horowitz, J. W. Kang, R. R. Dasari, and I. Barman, “Raman spectroscopy provides a powerful diagnostic tool for accurate determination of albumin glycation,” PLoS One 7(2), e32406 (2012).
[Crossref] [PubMed]

Elka, A.

N. Kourkoumelis, I. Balatsoukas, V. Moulia, A. Elka, G. Gaitanis, and I. D. Bassukas, “Advances in the in Vivo Raman Spectroscopy of Malignant Skin Tumors Using Portable Instrumentation,” Int. J. Mol. Sci. 16(7), 14554–14570 (2015).
[Crossref] [PubMed]

Gaitanis, G.

N. Kourkoumelis, I. Balatsoukas, V. Moulia, A. Elka, G. Gaitanis, and I. D. Bassukas, “Advances in the in Vivo Raman Spectroscopy of Malignant Skin Tumors Using Portable Instrumentation,” Int. J. Mol. Sci. 16(7), 14554–14570 (2015).
[Crossref] [PubMed]

González, F. J.

Guevara, E.

Guo, S.

S. Guo, T. Bocklitz, U. Neugebauer, and J. Popp, “Common Mistakes in Cross-Validating Classification Models,” Anal. Methods 9(30), 4410–4417 (2017).
[Crossref]

Horowitz, G. L.

N. C. Dingari, G. L. Horowitz, J. W. Kang, R. R. Dasari, and I. Barman, “Raman spectroscopy provides a powerful diagnostic tool for accurate determination of albumin glycation,” PLoS One 7(2), e32406 (2012).
[Crossref] [PubMed]

Huang, G.-B.

G.-B. Huang, “Learning capability and storage capacity of two-hidden-layer feedforward networks,” IEEE Trans. Neural Netw. 14(2), 274–281 (2003).
[Crossref] [PubMed]

Hughes, G.

G. Hughes, “On the mean accuracy of statistical pattern recognizers,” IEEE Trans. Inf. Theory 14(1), 55–63 (1968).
[Crossref]

Kang, J. W.

N. C. Dingari, G. L. Horowitz, J. W. Kang, R. R. Dasari, and I. Barman, “Raman spectroscopy provides a powerful diagnostic tool for accurate determination of albumin glycation,” PLoS One 7(2), e32406 (2012).
[Crossref] [PubMed]

Kourkoumelis, N.

N. Kourkoumelis, I. Balatsoukas, V. Moulia, A. Elka, G. Gaitanis, and I. D. Bassukas, “Advances in the in Vivo Raman Spectroscopy of Malignant Skin Tumors Using Portable Instrumentation,” Int. J. Mol. Sci. 16(7), 14554–14570 (2015).
[Crossref] [PubMed]

Luevano-Contreras, C.

Moulia, V.

N. Kourkoumelis, I. Balatsoukas, V. Moulia, A. Elka, G. Gaitanis, and I. D. Bassukas, “Advances in the in Vivo Raman Spectroscopy of Malignant Skin Tumors Using Portable Instrumentation,” Int. J. Mol. Sci. 16(7), 14554–14570 (2015).
[Crossref] [PubMed]

Neugebauer, U.

S. Guo, T. Bocklitz, U. Neugebauer, and J. Popp, “Common Mistakes in Cross-Validating Classification Models,” Anal. Methods 9(30), 4410–4417 (2017).
[Crossref]

Pasini, A.

A. Pasini, “Artificial neural networks for small dataset analysis,” J. Thorac. Dis. 7(5), 953–960 (2015).
[PubMed]

Popp, J.

S. Guo, T. Bocklitz, U. Neugebauer, and J. Popp, “Common Mistakes in Cross-Validating Classification Models,” Anal. Methods 9(30), 4410–4417 (2017).
[Crossref]

Ramírez-Elías, M. G.

Torres-Galván, J. C.

Vapnik, V.

C. Cortes and V. Vapnik, “Support-vector networks,” Mach. Learn. 20(3), 273–297 (1995).
[Crossref]

Anal. Methods (1)

S. Guo, T. Bocklitz, U. Neugebauer, and J. Popp, “Common Mistakes in Cross-Validating Classification Models,” Anal. Methods 9(30), 4410–4417 (2017).
[Crossref]

Biomed. Opt. Express (1)

IEEE Trans. Inf. Theory (1)

G. Hughes, “On the mean accuracy of statistical pattern recognizers,” IEEE Trans. Inf. Theory 14(1), 55–63 (1968).
[Crossref]

IEEE Trans. Neural Netw. (1)

G.-B. Huang, “Learning capability and storage capacity of two-hidden-layer feedforward networks,” IEEE Trans. Neural Netw. 14(2), 274–281 (2003).
[Crossref] [PubMed]

Int. J. Mol. Sci. (1)

N. Kourkoumelis, I. Balatsoukas, V. Moulia, A. Elka, G. Gaitanis, and I. D. Bassukas, “Advances in the in Vivo Raman Spectroscopy of Malignant Skin Tumors Using Portable Instrumentation,” Int. J. Mol. Sci. 16(7), 14554–14570 (2015).
[Crossref] [PubMed]

J. Thorac. Dis. (1)

A. Pasini, “Artificial neural networks for small dataset analysis,” J. Thorac. Dis. 7(5), 953–960 (2015).
[PubMed]

Mach. Learn. (1)

C. Cortes and V. Vapnik, “Support-vector networks,” Mach. Learn. 20(3), 273–297 (1995).
[Crossref]

PLoS One (1)

N. C. Dingari, G. L. Horowitz, J. W. Kang, R. R. Dasari, and I. Barman, “Raman spectroscopy provides a powerful diagnostic tool for accurate determination of albumin glycation,” PLoS One 7(2), e32406 (2012).
[Crossref] [PubMed]

Other (3)

T. Hastie, R. Tibshirani, and J. Friedman, “The Elements of Statistical Learning. Data Mining, Inference, and Prediction”, 2nd edition (2008).

I.N. da Silva, “Artificial Neural Networks, A Practical Course,” (Springer 2017).

R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2nd ed., Wiley (2001).

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