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Gastroesophageal reflux disease danger factor determining method based on machine learning and system thereof

A gastroesophageal reflux and risk factor technology, applied in the field of methods and systems for determining risk factors of gastroesophageal reflux disease, can solve problems such as low accuracy, and achieve the effect of reducing the incidence rate

Active Publication Date: 2019-04-26
南京市中西医结合医院
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a method and system for determining risk factors of gastroesophageal reflux disease based on machine learning to solve the problem of low accuracy in the prior art when using statistics to determine risk factors for gastroesophageal reflux disease

Method used

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  • Gastroesophageal reflux disease danger factor determining method based on machine learning and system thereof
  • Gastroesophageal reflux disease danger factor determining method based on machine learning and system thereof
  • Gastroesophageal reflux disease danger factor determining method based on machine learning and system thereof

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Embodiment 1

[0049] figure 1 It is a schematic flowchart of a method for determining risk factors for gastroesophageal reflux disease based on machine learning in an embodiment of the present invention, such as figure 1 As shown, the method for determining risk factors of gastroesophageal reflux disease based on machine learning provided by the embodiment of the present invention specifically includes the following steps.

[0050] Step 101: Construct a user information set; the user information set is a data set with M rows and N columns; the factor in the i-th row and the first column in the user information set is the ID number of the user questionnaire, and the factors in the first column in different rows Factors are represented as different user questionnaire ID numbers; the factors in the first row and column j of the user information set are questionnaire questions, and the factors in the first row in different columns are expressed as different questions; the user information set ...

Embodiment 2

[0097] To achieve the above object, the present invention also provides a figure 2 The machine learning-based risk factor determination system for gastroesophageal reflux disease shown. The system includes:

[0098] The user information set construction module 100 is used to construct the user information set; the user information set is a data set of M rows and N columns; the factor in the i-th row and the first column in the user information set is the user questionnaire ID number, and different The factors in the first column in the row are represented as different user questionnaire ID numbers; the factors in the first row and column j in the user information set are the questions of the questionnaire, and the factors in the first row in different columns are represented as different questions ; The factors in the i-th row and j-th column in the user information set are the answers of the i-th user questionnaire ID number to the j-th question; wherein, 2≤i≤M, 2≤j≤N.

[...

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Abstract

The invention discloses a gastroesophageal reflux disease danger factor determining method based on machine learning and a system thereof. The method and the system settle a problem of low accuracy indetermining the gastroesophageal reflux disease danger factor according to statistics. The method comprises the steps of constructing a user information set which comprises the gastroesophageal reflux disease danger factor, performing quantification processing on the factor in the user information set, and obtaining a quantification data matrix; secondly, performing standardization on the quantification data matrix, performing dimension reducing processing on the standardized matrix according to a main component analysis algorithm; then clustering the data in the processed data set by means of a hierarchy clustering algorithm, and obtaining a hierarchy clustering dendrogram; then according to a clustering number which is determined by the hierarchy clustering dendrogram, performing clustering dividing on the data in the processed data set according to the clustering number, and obtaining a plurality of class clusters; and finally calculating a correlation index among elements in eachclass cluster, and determining the element with highest correlation index as the gastroesophageal reflux disease danger factor.

Description

technical field [0001] The present invention relates to the field of machine learning and medical technology, in particular to a method and system for determining risk factors of gastroesophageal reflux disease based on machine learning. Background technique [0002] Gastroesophageal reflux disease is a common digestive system disease worldwide, and its incidence is showing an increasing trend year by year. Therefore, the treatment of gastroesophageal reflux disease should arouse our sufficient attention. Since the occurrence of gastroesophageal reflux disease is closely related to lifestyle, emotional changes, eating habits, etc., and the condition is easily changed, collecting a large amount of data and analyzing the characteristics of the data plays an important role in the study of the disease and its prevention. [0003] At present, it is rare to use machine learning methods to extract risk factors in the diagnostic technology of gastroesophageal reflux disease. Learn...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H50/20
CPCG16H50/20
Inventor 刘万里徐雷黄玉珍姚澜李荣臻夏吉安
Owner 南京市中西医结合医院
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