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Diabetes syndrome prediction system

A technology for predicting systems and diabetes, applied in biological neural network models, patient-specific data, health index calculations, etc., can solve problems such as improving clinical efficacy, and achieve good classification results

Pending Publication Date: 2020-02-28
CHENGDU UNIV OF TRADITIONAL CHINESE MEDICINE
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  • Abstract
  • Description
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  • Application Information

AI Technical Summary

Problems solved by technology

Western medicine often adopts comprehensive prevention and treatment measures for this disease, and traditional Chinese medicine also shows a good prospect in the treatment of this disease, but the biggest problem at present is still how to further improve the clinical efficacy

Method used

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Experimental program
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Effect test

Embodiment 1

[0035] Embodiment 1 The composition and workflow of the diagnostic module in the prediction system of the present invention

[0036] The prediction system of the present invention includes an input module, a diagnosis module and an output module.

[0037] Among them, the diagnostic module includes: a preprocessing module for upgrading the data, and a convolutional neural network module for convolutional neural network analysis of the upgraded data. The following is a brief description of the workflow of the diagnostic module:

[0038] 1. Preprocessing module

[0039] 1. Data preprocessing of gender, age and disease course

[0040] After the user inputs the gender, age, and disease course (in years) of the diabetic patient through the information input module, the preprocessing module of the diagnostic module converts the input data into attribute data:

[0041] (1) Gender attribute: use 0 for female, 1 for male, and x 1 Said.

[0042] (2) Age attribute: The original data is the data repr...

experiment example 1

[0075] Experimental Example 1 Comparison of the prediction effect of the prediction system of the present invention

[0076] The inventor surveyed 300 diabetic patients, and when the patients knew the purpose of the survey information, they collected statistics on the type of syndrome, gender, age, disease course (years), and various symptoms actually diagnosed by the doctor.

[0077] The symptoms include: dry mouth, fatigue, thirst and polydipsia, blurred vision, numbness of the limbs, dizziness, dark red tongue, and greasy fetus.

[0078] The aforementioned information is randomly divided into 2 subsets: 80% is used as the training set, and the other 20% is used as the test set. The system of the present invention establishes a CNN model based on the training set, and then uses the test set to predict syndromes.

[0079] Taking the test set data classification accuracy as an indicator, the experimental results in Table 3 are obtained.

[0080] Table 3 Forecast results

[0081]

[0082...

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Abstract

The invention provides a diabetes syndrome prediction system, and belongs to the field of disease syndrome prediction systems. According to the invention, a convolutional neural network model is constructed by using a deep learning algorithm, and the model can predict diabetes syndrome characteristics by using simple information such as gender, age and course of disease of the diabetes patient, and the accuracy is relatively high. The algorithm is built in a diabetes syndrome prediction system, so the method is convenient to apply and has a good prospect.

Description

Technical field [0001] The invention relates to the field of disease syndrome prediction systems, in particular to a diabetes syndrome prediction system. Background technique [0002] Diabetes is one of the three chronic non-communicable diseases threatening human health in the world. Western medicine mostly adopts comprehensive prevention and treatment measures for this disease. Traditional Chinese medicine also shows a good prospect in the treatment of this disease. However, the biggest problem at present is still how to further improve the clinical efficacy. To further improve the ability of Chinese medicine to prevent and treat diabetes, it is necessary to further strengthen the accuracy of syndrome diagnosis, including quick and accurate judgment of its syndrome category. [0003] The development of computer artificial intelligence technology has brought opportunities for the study of complex life phenomena, and provided feasible conditions for the rapid and accurate judgment...

Claims

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Application Information

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IPC IPC(8): G16H50/70G16H50/30G16H10/60G06N3/04
CPCG16H50/70G16H50/30G16H10/60G06N3/045
Inventor 胡绿慧温川飙叶桦李凡廖柳城胡远樟
Owner CHENGDU UNIV OF TRADITIONAL CHINESE MEDICINE
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