A personalized diet recommendation method for diabetes mellitus by introducing adaboost probability matrix decomposition

Abstract

The invention discloses a method of introducing Adaboost probability matrix decomposition for diabetes personalized diet recommendation, comprising: step 1, establishing a dietary preference feature set U={u for diabetic patients 1 , u 2 ,...,u n} and the attribute feature set of food V={v 1 ,v 2 ,...,v m}, record the diet of diabetic patients, extract preference features and food attribute features, and form the dietary preference matrix U∈R of diabetic patients K×M and food attribute features V ∈ R K×N Step 2, determine the correlation strength between the dietary preference of the diabetic patient and the attribute characteristics of the food by quantifying the correlation between the dietary preference of the diabetic patient and the attribute characteristics of the food; Step 3, weighting the degree of association After the value distribution is basically classified, the training data set updates the weight distribution, and assigns weights to all the correlation degrees to classify and exclude unnecessary foods, and obtains the final correlation degree classification as follows: Step 4. According to the conditional probability and the correlation degree Categorize the personalized diet.

Description

technical field

[0001] The invention relates to the field of intelligent medicine, in particular to a personalized diet recommendation method for diabetes by introducing Adaboost probability matrix decomposition. Background technique

[0002] With the gradual acceleration of the Internet age, the amount of information has skyrocketed, and the technology of providing recommendation services for users has also been applied to various fields, so as to help users find the information they want more accurately.

[0003] The current diet recommendation methods for diabetic patients mainly include association rule-based recommendation, content-based recommendation, collaborative filtering recommendation, constraint-based recommendation and other methods: (1) The main representative of association rule-based recommendation is the Apriori algorithm, and its core idea is Strong association rules are generated in the frequency set, and the defined rules must meet the minimum confidence...

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