Individualized diabetic diet recommendation method by introducing Adaboost probability matrix decomposition

Abstract

The invention discloses an individualized diabetic diet recommendation method by introducing Adaboost probability matrix decomposition. The method comprises the following steps: 1, establishing a foodpreference characteristic set U={u1, u2, ..., un} of a diabetic patient and a food attribute characteristic set V={v1, v2, ..., vm}, recording diets of the diabetic patient, extracting preference characteristics and food attribute characteristics, and forming a food preference matrix U belong to RK*M of the diabetic patient and the food attribute characteristic V belong to RK*N; 2, determining association strength between the food preference of the diabetic patient and the attribute characteristics of the foods by using association degree quantification between the food preference of the diabetic patient and the attribute characteristics of the foods; 3, performing weight distributing on the association degree to obtain basic classification, updating the weight distribution by a trainingdata set, endowing all the association degrees with the weights to be classified, excluding unnecessary foods, and obtaining the final following association degree classification shown in the description; 4, classifying according to conditional probability and the association degree classification, thereby obtaining the individualized 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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