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XGBoost disease probability predicting method, system and storage medium

A probabilistic prediction and disease technology, applied in the field of disease prediction, can solve the problems of high accuracy and high timeliness prediction of massive case data, achieve accurate disease diagnosis services, reduce misdiagnosis rate, and reduce medical accidents

Inactive Publication Date: 2019-08-09
闻康集团股份有限公司
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  • Abstract
  • Description
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  • Application Information

AI Technical Summary

Problems solved by technology

[0003] To this end, the embodiment of the present invention provides an XGBoost disease probability prediction method, system and storage medium to solve the problem that existing disease prediction methods cannot predict massive case data with high precision and high timeliness

Method used

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  • XGBoost disease probability predicting method, system and storage medium
  • XGBoost disease probability predicting method, system and storage medium
  • XGBoost disease probability predicting method, system and storage medium

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Embodiment 1

[0033] Such as figure 1 As shown, this embodiment proposes a XGBoost disease probability prediction method, which method includes:

[0034] S100. Acquire original case data, where the original case data includes various disease information and symptom information corresponding to the disease information. This embodiment uses 4920 pieces of case data, in which the diagnosis record of each patient is one piece of data, there are 132 symptoms, 41 kinds of diseases, and 120 pieces of data for each disease.

[0035] S200, performing 0-1 standardization processing on the original case data to obtain a sample data set, and cutting the sample data set into a training set and a test set.

[0036] The disease and symptom information in the original case data is stored in text data format, which needs to be standardized from 0 to 1, and the text information is mapped to the [0,1] interval. For example, if a patient has a certain symptom, the value is assigned to 1 , without this sympto...

Embodiment 2

[0067] Corresponding to the above-mentioned embodiment 1, this embodiment also provides an XGBoost disease probability prediction system, which includes: a processor and a memory;

[0068] memory for storing one or more program instructions;

[0069] The processor is configured to run one or more program instructions to execute any method step in an XGBoost disease probability prediction method introduced in the above embodiment.

[0070] Optionally, the system may further include a display for visually displaying the disease probability prediction value obtained according to the XGBoost disease probability prediction method.

Embodiment 3

[0072] Corresponding to Embodiment 2 above, this embodiment further provides a computer storage medium, where the computer storage medium includes one or more program instructions. Wherein, one or more program instructions are used for an XGBoost disease probability prediction system to execute an XGBoost disease probability prediction method as introduced above.

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Abstract

An embodiment of the invention discloses an XGBoost disease probability predicting method, a system and a storage medium. The method comprises the steps of acquiring original case data; performing 0-1standardizing processing on the original case data for obtaining a sample dataset, and dividing the sample dataset into a training set and a testing set; constructing an XGBoost multi-class model, and setting an initial model parameter; training the XGBoost multi-class model by means of the training set; testing the trained XGBoost multi-class model by means of the testing set, outputting diseaseprobability values which correspond with multiple diseases; performing threshold selection on the disease probability value, and outputting an estimated disease probability value. The XGBoost diseaseprobability predicting method supports intelligent analysis and prediction to mass patient data and has advantages of high accuracy, high timeliness, simple operation and low cost.

Description

technical field [0001] The embodiments of the present invention relate to the technical field of disease prediction, and in particular to an XGBoost disease probability prediction method, system and storage medium. Background technique [0002] Medical resources are scarce for everyone. In order to ensure effective diagnosis of ailments, patients hope to receive the best expert treatment regardless of major or minor illnesses. However, due to the high threshold of medical training, it is difficult to train an excellent doctor. The cost is very high, the number of professional medical personnel is small, and there is a problem of uneven distribution of medical resources. At present, there is no very accurate method for multi-category probability prediction of diseases. The existing disease prediction methods are still at the stage of speculating diseases through symptoms. There is no way to start with the big data of thousands of cases, so there is an urgent need for a time-...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H50/20G06K9/62
CPCG16H50/20G06F18/214
Inventor 黄海涛郑早明肖俊许高峰王婧
Owner 闻康集团股份有限公司
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