A DNA-Based Intelligent Expert System for Personalised Skin-Health Recommendations

Intensive attention on personalised skin-health solutions is on account of incomparable love of skin and an urgent need for effective treatment. In the meanwhile, people have great expectations on how to utilise genetic knowledge of our body to provide a precise solution for different individuals, such as daily use of skin-health products, since the rapid development of genetic test services and skin-health science. However, the complexity of multi-modal data, the establishment of correlations between consumer genetic data and product ingredients are the main obstacles encountered today. Determining to settle such obstacles, a personalised recommendation expert system for selecting optimised skin-health product within the category based upon genetic phenotypes for each consumer was introduced in this article. Random Forests were implemented to achieve automatic product categorisation, the performance discussed and compared with SVM and Logistic Regression. Lastly, categorised skin-health product suggestion was made with an optimised recommendation model based on associated genetic phenotype information. Potential changes (up to 71.0% more phenotypic relevant ingredients) from experiments using real product data were demonstrated and compared with imitated cases of real-life human selections.

[1]  Ciprian Dobre,et al.  Expert system for nutrition care process of older adults , 2018, Future Gener. Comput. Syst..

[2]  C. Toumazou,et al.  Simultaneous DNA amplification and detection using a pH-sensing semiconductor system , 2013, Nature Methods.

[3]  C. Shuman,et al.  Direct‐to‐consumer genetic testing: good, bad or benign? , 2010, Clinical genetics.

[4]  J. Maynard Smith.  The units of selection. , 2021, Novartis Foundation symposium.

[5]  Davide Buscaldi,et al.  A Random Forest Approach for Authorship Profiling , 2015, CLEF.

[6]  Yuichi Tsumaki,et al.  Development of a skincare robot , 2008, 2008 IEEE International Conference on Robotics and Automation.

[7]  Zviad Kirtava,et al.  e-Health/m-Health services for dermatology outpatients screening for skin cancer and follow-up , 2016, 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom).

[8]  C. R. J. Amalraj,et al.  Expert System For Diagnosis Of Skin Diseases , 2015 .

[9]  Richard C Lewontin,et al.  An Introduction to Genetic Statistics , 1958 .

[10]  Józef Banas,et al.  List of functions , 2013 .

[11]  Mahesh Pal,et al.  Multiclass Approaches for Support Vector Machine Based Land Cover Classification , 2008, ArXiv.

[12]  Li Jin,et al.  A Genome-Wide Association Study of Basal Transepidermal Water Loss Finds that Variants at 9q34.3 Are Associated with Skin Barrier Function. , 2017, The Journal of investigative dermatology.

[13]  D. Ruppert.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

[14]  Ji Jia,et al.  RIO-40C: A Low-Cost Wearable Sunlight Exposure Monitor for Skincare: Demo Abstract , 2017, IoTDI.

[15]  Lucy E. Dunne,et al.  Personalised health management systems: The integration of innovative sensing, textile, information and communication technologies , 2005 .

[16]  Wen-Huang Cheng,et al.  A Cloud-based Intelligent Skin and Scalp Analysis System , 2018, 2018 IEEE Visual Communications and Image Processing (VCIP).

[17]  W. Meurer,et al.  Logistic Regression: Relating Patient Characteristics to Outcomes. , 2016, JAMA.

[18]  Catherine M. Phillips,et al.  Nutrigenetics and Metabolic Disease: Current Status and Implications for Personalised Nutrition , 2013, Nutrients.

[19]  Allan Donner,et al.  Classification Efficiency of Multinomial Logistic Regression Relative to Ordinal Logistic Regression , 1989 .

[20]  Lori B. Andrews,et al.  Assessing Genetic Risks: Implications for Health and Social Policy , 1994 .

[21]  Ram Mohana Reddy Guddeti,et al.  Performance analysis of Ensemble methods on Twitter sentiment analysis using NLP techniques , 2015, Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015).

[22]  Shuicheng Yan,et al.  "Wow! you are so beautiful today!" , 2013, MM '13.

[23]  Dorothea Heiss-Czedik,et al.  An Introduction to Genetic Algorithms. , 1997, Artificial Life.

[24]  Christofer Toumazou,et al.  PERSON—Personalized Expert Recommendation System for Optimized Nutrition , 2018, IEEE Transactions on Biomedical Circuits and Systems.

[25]  Mark Atkinson,et al.  Large-scale genetic fine mapping and genotype-phenotype associations implicate polymorphism in the IL2RA region in type 1 diabetes , 2007, Nature Genetics.

[26]  Songsri Tangsripairoj,et al.  SkinProf: An Android Application for Smart Cosmetic and Skincare Users , 2018, 2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE).

[27]  F B Churchill,et al.  William Johannsen and the genotype concept , 1974, Journal of the history of biology.

[28]  Feng Shu,et al.  A wearable sensor system for neonatal seizure monitoring , 2017, 2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN).

[29]  Johnathan Mun.  List of Functions , 2012 .

[30]  Lior Rokach,et al.  Ensemble-based classifiers , 2010, Artificial Intelligence Review.

[31]  M. Uhlén,et al.  Single-nucleotide polymorphism analysis by pyrosequencing. , 2000, Analytical biochemistry.

[32]  Darcy A. Davis,et al.  Bringing Big Data to Personalized Healthcare: A Patient-Centered Framework , 2013, Journal of General Internal Medicine.

[33]  Luca Scrucca,et al.  GA: A Package for Genetic Algorithms in R , 2013 .

[34]  Vice President,et al.  An Introduction to Expert Systems , 1989 .

[35]  Kristin L. Sainani,et al.  Logistic Regression , 2014, PM & R : the journal of injury, function, and rehabilitation.

[36]  P. Donnelly,et al.  Replicating genotype–phenotype associations , 2007, Nature.

[37]  Saleena Khanna,et al.  iSkin specialist — A big data based expert system for dermatology , 2017, 2017 IEEE International Conference on Big Data (Big Data).

[38]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[39]  C. B. Lucasius,et al.  Understanding and using genetic algorithms Part 2. Representation, configuration and hybridization , 1994 .

[40]  Z. Draelos,et al.  The skin health and beauty pyramid: a clinically based guide to selecting topical skincare products. , 2014, Journal of drugs in dermatology : JDD.

[41]  Hakan Ferhatosmanoglu,et al.  Short text classification in twitter to improve information filtering , 2010, SIGIR.

[42]  Michael Cariaso,et al.  SNPedia: a wiki supporting personal genome annotation, interpretation and analysis , 2011, Nucleic Acids Res..

[43]  Seyed Taghi Akhavan Niaki,et al.  Optimizing a hybrid vendor-managed inventory and transportation problem with fuzzy demand: An improved particle swarm optimization algorithm , 2014, Inf. Sci..

[44]  Jeffrey Dean,et al.  Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.

[45]  Sabine Süsstrunk,et al.  Color match: an imaging based mobile cosmetics advisory service , 2008, Mobile HCI.

[46]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[47]  H. Schwanitz,et al.  Skin care management: educational aspects , 2003, International archives of occupational and environmental health.

[48]  Yuan-Ting Zhang,et al.  Guest Editorial on the Special Issue on Integrating Informatics and Technology for Precision Medicine , 2019, IEEE J. Biomed. Health Informatics.

[49]  W. Johannsen,et al.  The Genotype Conception of Heredity , 1911, The American Naturalist.

[50]  Robert Tibshirani,et al.  The Entire Regularization Path for the Support Vector Machine , 2004, J. Mach. Learn. Res..

[51]  Harry Wechsler,et al.  Spam Detection using Clustering, Random Forests, and Active Learning , 2009 .

[52]  W. Rakowski,et al.  Stage-based expert systems to guide a population of primary care patients to quit smoking, eat healthier, prevent skin cancer, and receive regular mammograms. , 2005, Preventive medicine.

[53]  Rashi Singh,et al.  Skin texture analysis using machine learning , 2016, 2016 Conference on Advances in Signal Processing (CASP).