Establishment method of disease risk prediction model and recommendation method of disease insurance product

Abstract

The invention relates to an establishment method of a disease risk prediction model and a recommendation method of a disease insurance product. The establishment method comprises the steps: obtaininghistorical diagnosis and treatment data of medical insurance insured personnel in a preset region; performing classified sampling processing on the historical diagnosis and treatment data to obtain asample data set, wherein each piece of sample data comprises historical disease diagnosis coding information of each sample in a preset time range; preprocessing the sample data set to remove invaliddata; clustering historical disease diagnosis coding information of all samples in the preprocessed sample data set according to disease attributes and focus parts to obtain disease clustering featuretags; screening the disease clustering feature tags by adopting a preset feature selection algorithm to obtain serious disease clustering feature tags, and establishing a disease risk prediction model corresponding to the preset serious disease according to the serious disease clustering feature label, the gender, the age and the doctor-seeing behavior information in combination with an extreme gradient boosting algorithm, thereby laying a foundation for the accuracy of disease insurance product recommendation.

Description

technical field [0001] The invention relates to the insurance field, in particular to a method for establishing a disease risk prediction model and a method for recommending disease insurance products. Background technique [0002] With the development and needs of society, consumers' awareness of insurance has gradually improved. Among them, consumers' demand for insurance is also developing in a more refined direction, and the simple product pricing method based on the two dimensions of age and gender is more mechanical. [0003] At present, in the insurance industry, the risk models or rules used to judge the health risks of customers are often sorted out based on the traditional experience of the insurance industry, and it is impossible to rule out the situation of concealing health status for reverse insurance. The recommended insurance products often have the disadvantage of low accuracy. Contents of the invention [0004] In view of this, a method for establishing...

AI Technical Summary

Problems solved by technology

[0003] At present, in the insurance industry, the risk models or rules used to judge the health risks of customers are often sorted out based on the traditional experience of the ins

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