Human Skin Profiling by Physical Skin Biomarkers: A Machine Learning Approach

  • Davoud Rahimi ArdaliEmail author
  • Lars Rüether
  • Viktor Popov
  • Gerrit Schlippe
  • Branislav Vuksanovic
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 797)


Personalized skincare requires customization of products, which are adapted in a way to suit patient’s skin profile. The process of customization involves several major steps: (i) determine the information which will be used to successfully distinguish between patient’s skin profiles and the methods to obtain the information, (ii) develop an efficient algorithm which will accurately and efficiently classify the patients according to the information used to distinguish between skin profiles, (iii) determine the correct or most efficient skincare formula to suit the particular skin profile. This study considers the first two steps. First, it was examined whether patient’s physical skin measurements can be used to determine the patient’s skin profile. In the second step, twenty machine learning algorithms, which were selected after initial screening, were employed for the classification task. The study showed that the use of physical skin measurements to distinguish between skin profiles represents a promising option. It was also shown that some of the machine learning algorithms are particularly suitable for classification tasks of this type. Sensitivity of the selected classification algorithms to the location on the skin which is sampled, i.e. affected or unaffected part of skin for volunteers with diabetes mellitus type II and rosacea, is reported.


Personalized skincare Physical skin biomarkers Classification of skin profiles Machine learning algorithms Skin profiling 



The present study was partially supported by the FP7 EC Programme, Theme FoF.NMP.2013-6 Mini-factories for customized products using local flexible production, Grant agreement no: 609198.


  1. 1.
    Altman NS (1992) An introduction to kernel and nearest-neighbor nonparametric regression. Am Stat 46:175–185MathSciNetGoogle Scholar
  2. 2.
    Bouveyron C (2014) Adaptive mixture discriminant analysis for supervised learning with unobserved classes. J Classif 31:49–84MathSciNetCrossRefGoogle Scholar
  3. 3.
    Breiman L (2001) Random forests. Mach Learn 45:5–32CrossRefGoogle Scholar
  4. 4.
    Friedman JH (1991) Multivariate adaptive regression splines. Ann Stat 1–67MathSciNetCrossRefGoogle Scholar
  5. 5.
    Garthwaite PH (1994) An interpretation of partial least squares. J Am Stat Assoc 89:122–127MathSciNetCrossRefGoogle Scholar
  6. 6.
    Geurts P, Ernst D, Wehenkel L (2006) Extremely randomized trees. Mach Learn 63:3–42CrossRefGoogle Scholar
  7. 7.
    Grubinger T, Zeileis A, Pfeiffer K-P (2011) EVTREE: evolutionary learning of globally optimal classification and regression trees in R. Department of Economics (Inst. für Wirtschaftstheorie und Wirtschaftsgeschichte)Google Scholar
  8. 8.
    Hastie T, Buja A, Tibshirani R (1995) Penalized discriminant analysis. Ann Stat 73–102MathSciNetCrossRefGoogle Scholar
  9. 9.
    Hsu C-W, Chang C-C, Lin C-J (2003) A practical guide to support vector classificationGoogle Scholar
  10. 10.
    Izenman AJ (2013) Linear discriminant analysis. In: Modern multivariate statistical techniques. Springer, pp 237–280Google Scholar
  11. 11.
    Kohonen T (1995) Learning vector quantization. In: Self-organizing maps. Springer, pp 175–189Google Scholar
  12. 12.
    Kononenko I (2001) Machine learning for medical diagnosis: history, state of the art and perspective. Artif Intell Med 23:89–109. Scholar
  13. 13.
    Kuhn M (2008) Caret package. J Stat Softw 28:1–26CrossRefGoogle Scholar
  14. 14.
    Melssen W, Wehrens R, Buydens L (2006) Supervised Kohonen networks for classification problems. Chemom Intell Lab Syst 83:99–113CrossRefGoogle Scholar
  15. 15.
    Pang H, Tong T, Zhao H (2009) Shrinkage-based diagonal discriminant analysis and its applications in high-dimensional data. Biometrics 65:1021–1029MathSciNetCrossRefGoogle Scholar
  16. 16.
    Qin B, Xia Y, Wang S, Du X (2011) A novel Bayesian classification for uncertain data. Knowl Based Syst 24:1151–1158CrossRefGoogle Scholar
  17. 17.
    Quinlan JR (1996) Improved use of continuous attributes in C4. 5. J Artif Intell Res 4:77–90CrossRefGoogle Scholar
  18. 18.
    R Core Team (2014) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
  19. 19.
    Ripley BD (2007) Pattern recognition and neural networks. Cambridge university pressGoogle Scholar
  20. 20.
    Salgado CM, Vieira SM, Mendonça LF, Finkelstein S, Sousa JMC (2016) Ensemble fuzzy models in personalized medicine: application to vasopressors administration. Eng Appl Artif Intell 49:141–148. Scholar
  21. 21.
    Savard J (2013) Personalised medicine: a critique on the future of health care. J Bioeth Inq 10:197–203. Scholar
  22. 22.
    Scholkopf B, Sung K-K, Burges CJC, Girosi F, Niyogi P, Poggio T, Vapnik V (1997) Comparing support vector machines with Gaussian kernels to radial basis function classifiers. IEEE Trans Signal Process 45:2758–2765CrossRefGoogle Scholar
  23. 23.
    Strempel S, Nendza M, Scheringer M, Hungerbühler K (2013) Using conditional inference trees and random forests to predict the bioaccumulation potential of organic chemicals. Environ Toxicol Chem 32:1187–1195CrossRefGoogle Scholar
  24. 24.
    Therneau TM, Atkinson EJ (1997) An introduction to recursive partitioning using the RPART routine. Stats 116:1–52Google Scholar
  25. 25.
    Tsai WM, Zhang H, Buta E, O’Malley S, Gueorguieva R (2016) A modified classification tree method for personalized medicine decisions. Stat Interface 9:239–253. Scholar
  26. 26.
    Weiss JC, Natarajan S, Peissig PL, McCarty CA, Page D (2012) Machine learning for personalized medicine: predicting primary myocardial infarction from electronic health records. AI Mag 33:33. Scholar
  27. 27.
    Xu B, Huang JZ, Williams G, Ye Y (2012) Hybrid weighted random forests for classifying very high-dimensional data. Int J Data Warehous Min 8:44–63CrossRefGoogle Scholar
  28. 28.
    Zeevi D, Korem T, Zmora N, Israeli D, Rothschild D, Weinberger A, Ben-Yacov O, Lador D, Avnit-Sagi T, Lotan-Pompan M, Suez J, Mahdi JA, Matot E, Malka G, Kosower N, Rein M, Zilberman-Schapira G, Dohnalová L, Pevsner-Fischer M, Bikovsky R, Halpern Z, Elinav E, Segal E (2015) Personalized nutrition by prediction of glycemic responses. Cell 163:1079–1095. Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.University of PortsmouthPortsmouthUK
  2. 2.Dermatest GmbHMünsterGermany
  3. 3.Ascend Technologies Ltd.SouthamptonUK

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