Literature DB >> 32149660

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

Xiaoran Liu, Chih-Han Chen, Maria Karvela, Christofer Toumazou.   

Abstract

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.

Entities:  

Year:  2020        PMID: 32149660     DOI: 10.1109/JBHI.2020.2978667

Source DB:  PubMed          Journal:  IEEE J Biomed Health Inform        ISSN: 2168-2194            Impact factor:   5.772


  2 in total

1.  Skin Lesion Analysis for Melanoma Detection Using the Novel Deep Learning Model Fuzzy GC-SCNN.

Authors:  Usharani Bhimavarapu; Gopi Battineni
Journal:  Healthcare (Basel)       Date:  2022-05-23

2.  Real-Time Learning from an Expert in Deep Recommendation Systems with Application to mHealth for Physical Exercises.

Authors:  Arash Mahyari; Peter Pirolli; Jacqueline A LeBlanc
Journal:  IEEE J Biomed Health Inform       Date:  2022-08-11       Impact factor: 7.021

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.