Name
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/JBHI.2020.2978667 | IEEE Journal of Biomedical and Health Informatics |
Keywords | DocType | Volume |
DNA,Expert Systems,Humans,Skin | Journal | 24 |
Issue | ISSN | Citations |
11 | 2168-2194 | 1 |
PageRank | References | Authors |
0.35 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaoran Liu | 1 | 1 | 0.35 |
| Chih-Han Chen | 2 | 1 | 0.35 |
| Maria Karvela | 3 | 1 | 0.35 |
| Christofer Toumazou | 4 | 265 | 59.06 |