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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Problems solved by technology

Therefore, it is extremely difficult for the collaborative filtering recommendation method to deal with complex object attributes; (4) The constraint-based recommendation method mainly relies on the attributes of the items, that is, to recommend items that can match user needs, preference characteristics, and rigid requirements from the item collection. Some standards rigidly restrict the diet structure of diabetic patients, although it seems to meet the patient's medical index requirements and the patient's preference for food; in fact, it is not the case, if there is a demand conflict or empty solution during the constraint solving process , it may be more troublesome to deal with the problem based on constraint solving. Even if the follow-up improvement of the constraint-based recommendation method is to gradually relax the constraints by calculating the conflicting requirements to obtain the solution, although the requirement conflict and empty solution are avoided, but due to Relaxing the constraints may lead to the infiltration of individual foods that are medically incompatible with the dietary standards of a certain diabetic patient into the recommended data set

[0004] Due to the irregular changes in the physical condition indicators of diabetic patients due to various reasons, some of the above traditional methods have certain limitations: (1) The traditional diabetic diet recommendation method is easy to lose some valuable information for diabetic patients in food selection. Food; (2) The traditional diabetic diet recommendation method treats diabetic patients in a universal way, ignoring the particularity of the patient; (3) The traditional diabetic diet recommendation method cannot balance the medical indicators and the wishes of diabetic patients, or blindly To pursue the patient's dietary preference characteristics to achieve personalized requirements, or to focus on medical standards, ignoring the patient's personalized dietary preference characteristics, causing patients to be disgusted and unable to eat, showing the effect of "pseudo-recommendation"

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