food recommendation system using clustering analysis for diabetic patients
TRANSCRIPT
Food Recommendation System Using Clustering Analysis for Diabetic Patients
Maiyaporn Phanich, Phathrajarin Pholkul, and Suphakant PhimoltaresAdvanced Virtual and Intelligent Computing (AVIC) Research Center
Department of Mathematics, Faculty of Science, Chulalongkorn [email protected], [email protected],
L o g o
Background and Motivation1
Foundation Theoretical2
Data Preparation3
Food Clustering Analysis4
Results5
Outlines
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Food Recommendation System6
Discussion7
Conclusion8
Background and Motivation
L o g oBackground and Motivation
Diabetes Mellitus is a disorder in which blood sugar levels are abnormally high because the body does not produce enough insulin to meet its needs.
This can lead to many serious diabetes complications. Nutrition therapy is a major solution to prevent and control
diabetes by managing the nutrition intake. The Food Pyramid is one of the choices recommended to
the diabetic patients. Within the same food groups, there is still a dietary
diversity that can affect the diabetic patients.
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Foundation Theoretical
L o g oFoundation Theoretical
Clustering Analysis A method of unsupervised learning which groups
similar objects into the same group called cluster.Self-Organizing Maps (SOM)
A type of artificial neural network that is trained using unsupervised learning.
K-Means Clustering Most well known and commonly used partitioning
clustering methods.
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Data Preparation
L o g oData Preparation
Food Dataset
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“Nutritive values for Thai food” provided by Nutrition Division, Department of Health, Ministry of Public Health (Thailand).
L o g oData Preparation
Categorized by food characteristic The dataset was divided into
22 groups based on their characteristic and shortly named A to V.
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L o g oData Preparation
Categorized by nutrition for diabetes
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L o g oData Preparation
Feature Extraction by nutrient ranking The nutritionists were asked to rank the importance of
eighteen nutrients to diabetic patients. The top eight nutrients are selected as main features
to be included in the clustering analysis.
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Food Clustering Analysis
L o g oClustering Analysis
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L o g oClustering Analysis
K-Means Clustering of the SOM Clustered the SOM with different values of k ranging
from 15 to 20. The Davies-Bouldin index was used to evaluate the
optimal k-value. The index gives the smallest value for k = 19. Mapped each training data to the closest node and
assigned cluster number to that data.
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Results
L o g oResults – SOM Training
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Visualization of the SOM of nutritive values for Thai food in 100 grams edible portion dataset.
The average quantization error = 0.1268
The topographic error = 0.0103
L o g oResults – K-Means Clustering
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Visualization of the clustered map; (left) the cluster is assigned to a number between 1 and 19, (right) represent each cluster with different colors.
L o g oResults - Definition
ClusterDefinition Food
GroupLow High
C1 Fiber, Thiamine - NF, LF, AF
C2 Protein, Vitamin E, Thiamine - LF, AF
C3 Vitamin C Protein NF, LFC4 Vitamin E Energy NF, LFC5 Vitamin E Fat NFC6 Fat, Vitamin E - NF, LF,
AFC7 Vitamin C, Vitamin E Energy, Carbohydrate LF, AFC8 - Energy, Fat NF, AFC9 Carbohydrate - NFC10 Vitamin E Energy, Carbohydrate LF, AFC11 Thiamine - NF, LF,
AFC12 Vitamin C, Vitamin E Carbohydrate NF, LF,
AFC13 Vitamin C - NF, LF,
AFC14 Energy, Carbohydrate,
Fiber, Thiamine - NF, LF, AF
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
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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.
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