Matches in SemOpenAlex for { <https://semopenalex.org/work/W2891864191> ?p ?o ?g. }
Showing items 1 to 88 of
88
with 100 items per page.
- W2891864191 endingPage "52778" @default.
- W2891864191 startingPage "52766" @default.
- W2891864191 abstract "Suitable nutritional diets have been widely recognized as important measures to prevent and control non-communicable diseases (NCDs). However, there is little research on nutritional ingredients in food now, which are beneficial to the rehabilitation of NCDs. In this paper, we profoundly analyzed the relationship between nutritional ingredients and diseases by using data mining methods. First, more than 7000 diseases were obtained, and we collected the recommended food and taboo food for each disease. Then, referring to the China Food Nutrition , we used noise intensity and information entropy to find out which nutritional ingredients can exert positive effects on diseases. Finally, we proposed an improved algorithm named CVNDA_Red based on rough sets to select the corresponding core ingredients from the positive nutritional ingredients. To the best of our knowledge, this is the first study to discuss the relationship between nutritional ingredients in food and diseases through data mining based on rough set theory in China. The experiments on real-life data show that our method based on data mining improves the performance compared with the traditional statistical approach, with the precision of 1.682. In addition, for some common diseases, such as diabetes, hypertension and heart disease, our work is able to identify correctly the first two or three nutritional ingredients in food that can benefit the rehabilitation of those diseases. These experimental results demonstrate the effectiveness of applying data mining in selecting of nutritional ingredients in food for disease analysis." @default.
- W2891864191 created "2018-09-27" @default.
- W2891864191 creator A5002141694 @default.
- W2891864191 creator A5003568313 @default.
- W2891864191 creator A5007510363 @default.
- W2891864191 creator A5007681226 @default.
- W2891864191 creator A5028112215 @default.
- W2891864191 creator A5052697587 @default.
- W2891864191 creator A5062336190 @default.
- W2891864191 creator A5084270607 @default.
- W2891864191 date "2018-01-01" @default.
- W2891864191 modified "2023-09-24" @default.
- W2891864191 title "Mining of Nutritional Ingredients in Food for Disease Analysis" @default.
- W2891864191 cites W126543085 @default.
- W2891864191 cites W1440064335 @default.
- W2891864191 cites W1714869120 @default.
- W2891864191 cites W1844176275 @default.
- W2891864191 cites W2002171181 @default.
- W2891864191 cites W2035541174 @default.
- W2891864191 cites W2054931700 @default.
- W2891864191 cites W2056382745 @default.
- W2891864191 cites W2063348231 @default.
- W2891864191 cites W2068389315 @default.
- W2891864191 cites W2079199018 @default.
- W2891864191 cites W2113967056 @default.
- W2891864191 cites W2118615260 @default.
- W2891864191 cites W2314578140 @default.
- W2891864191 cites W2435959519 @default.
- W2891864191 cites W2560463465 @default.
- W2891864191 cites W2562251455 @default.
- W2891864191 cites W2588816489 @default.
- W2891864191 cites W2598638843 @default.
- W2891864191 cites W2738584869 @default.
- W2891864191 cites W2793115047 @default.
- W2891864191 cites W2801760242 @default.
- W2891864191 cites W353927053 @default.
- W2891864191 cites W634646383 @default.
- W2891864191 cites W831516024 @default.
- W2891864191 doi "https://doi.org/10.1109/access.2018.2866389" @default.
- W2891864191 hasPublicationYear "2018" @default.
- W2891864191 type Work @default.
- W2891864191 sameAs 2891864191 @default.
- W2891864191 citedByCount "6" @default.
- W2891864191 countsByYear W28918641912019 @default.
- W2891864191 countsByYear W28918641912020 @default.
- W2891864191 countsByYear W28918641912021 @default.
- W2891864191 countsByYear W28918641912023 @default.
- W2891864191 crossrefType "journal-article" @default.
- W2891864191 hasAuthorship W2891864191A5002141694 @default.
- W2891864191 hasAuthorship W2891864191A5003568313 @default.
- W2891864191 hasAuthorship W2891864191A5007510363 @default.
- W2891864191 hasAuthorship W2891864191A5007681226 @default.
- W2891864191 hasAuthorship W2891864191A5028112215 @default.
- W2891864191 hasAuthorship W2891864191A5052697587 @default.
- W2891864191 hasAuthorship W2891864191A5062336190 @default.
- W2891864191 hasAuthorship W2891864191A5084270607 @default.
- W2891864191 hasBestOaLocation W28918641911 @default.
- W2891864191 hasConcept C126322002 @default.
- W2891864191 hasConcept C2779134260 @default.
- W2891864191 hasConcept C41008148 @default.
- W2891864191 hasConcept C71924100 @default.
- W2891864191 hasConceptScore W2891864191C126322002 @default.
- W2891864191 hasConceptScore W2891864191C2779134260 @default.
- W2891864191 hasConceptScore W2891864191C41008148 @default.
- W2891864191 hasConceptScore W2891864191C71924100 @default.
- W2891864191 hasFunder F4320321001 @default.
- W2891864191 hasFunder F4320321878 @default.
- W2891864191 hasLocation W28918641911 @default.
- W2891864191 hasLocation W28918641912 @default.
- W2891864191 hasOpenAccess W2891864191 @default.
- W2891864191 hasPrimaryLocation W28918641911 @default.
- W2891864191 hasRelatedWork W2096946506 @default.
- W2891864191 hasRelatedWork W2130043461 @default.
- W2891864191 hasRelatedWork W2350741829 @default.
- W2891864191 hasRelatedWork W2358668433 @default.
- W2891864191 hasRelatedWork W2376932109 @default.
- W2891864191 hasRelatedWork W2382290278 @default.
- W2891864191 hasRelatedWork W2390279801 @default.
- W2891864191 hasRelatedWork W2748952813 @default.
- W2891864191 hasRelatedWork W2899084033 @default.
- W2891864191 hasRelatedWork W3004735627 @default.
- W2891864191 hasVolume "6" @default.
- W2891864191 isParatext "false" @default.
- W2891864191 isRetracted "false" @default.
- W2891864191 magId "2891864191" @default.
- W2891864191 workType "article" @default.