Food Recommendation System Using Clustering Analysis for Diabetic Patients Maiyaporn Phanich, Phathrajarin Pholkul, and Suphakant Phimoltares Advanced Virtual and Intelligent Computing (AVIC) Research Center Department of Mathematics, Faculty of Science, Chulalongkorn University [email protected], [email protected], [email protected]
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Food Recommendation System Using Clustering Analysis for Diabetic patients
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Food Recommendation System Using Clustering Analysis for Diabetic Patients
Maiyaporn Phanich, Phathrajarin Pholkul, and Suphakant PhimoltaresAdvanced Virtual and Intelligent Computing (AVIC) Research Center
C15 Vitamin C Energy, Protein NF, LFC16 Vitamin E Carbohydrate NF, LF,
AFC17 Vitamin C Vitamin E NF, LFC18 - Carbohydrate NF, LF,
AFC19 Fat, Vitamin E - NF, LF,
AF
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Food Recommendation System
L o g o
The FRS will recommend five sequential substitution foods to the patients by ranking from their proximal nutrients.
When the users choose:The NF, the FRS will recommend the substituted food only in the NF.The LF and AF, the FRS will recommend the substituted food only in the NF and LF.
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Food Recommendation System (FRS)
L o g oFood Recommendation System (FRS)
Analyzing Nutrition Distance
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Example of distance matrix.
Distance between food no.U002 and other foods in the cluster.
Discussion
L o g oDiscussion
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Conclusion
L o g oConclusion
Nutrition is the major key to control diabetes. But, the existing categorization mechanism is not efficient
for classifying the food group. This research aims to present the next step in clustering by
using the SOM algorithm along with K-mean clustering. For the result of this research, a good recommendation for
diabetes diet care is provided as a food recommendation system.
For the future work, we expect to improve our research including with proposed service, technology and algorithms to diabetes diet care in Thailand.