Medical data missing processing method, device and equipment based on multiple regression model

Abstract

The invention discloses a medical data missing processing method, device and equipment based on a multiple regression model, which can solve the technical problems of poor data filling quality and insufficient accuracy when the existing data filling method is used for filling data at present. The method comprises the steps of obtaining a missing tuple corresponding to medical data, determining a complete tuple matched with the medical data type corresponding to the missing tuple, wherein the missing tuple is composed of missing attributes and partial complete attributes, and the complete tuple is composed of complete attributes; generating a preset number of multiple regression models by utilizing the complete attributes contained in the complete tuple; determining a candidate filling attribute combination about the missing attribute in the missing tuple; and screening out a target candidate filling attribute combination with the minimum total fitting error on the multiple regression model from the candidate filling attribute combinations, and filling the missing tuple by using the target candidate filling attribute combination. The method and the device are suitable for filling the missing medical data.

Description

technical field [0001] The present application relates to the technical field of big data processing, in particular to a method, device and equipment for missing medical data based on a multiple regression model. Background technique [0002] With the rapid development of computer technology and storage devices, the amount of data has exploded, followed by various data quality problems, the most obvious of which is the problem of missing data. The presence of missing data obviously affects the performance of downstream analytical applications, such as clustering, classification, entity matching, etc., as well as the accuracy of statistical analysis, such as mean, variance, median, etc. It can be seen that it is particularly important to accurately fill in missing data. [0003] The existing data filling methods are to fill medical data according to constraints or statistical information. Constraint-based methods use the rules and constraints defined on the data set to gener...

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Problems solved by technology

[0004] In view of this, this application provides a method, device and equipment for missing medical data based on a multiple regression model, which can be used to solve the problems of poor data filling quality and insufficient accuracy when filling data in existing data filling methods question

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