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Diabetes risk prediction method, device, equipment and storage medium

A risk prediction and diabetes technology, applied in the field of machine learning, can solve problems such as no prediction and evaluation, and achieve the effect of improving efficiency and accuracy

Pending Publication Date: 2021-12-17
HEBEI UNIV OF ENG
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, there is no corresponding predictive evaluation technology

Method used

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  • Diabetes risk prediction method, device, equipment and storage medium
  • Diabetes risk prediction method, device, equipment and storage medium
  • Diabetes risk prediction method, device, equipment and storage medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0048] This embodiment provides a diabetes risk prediction method, such as figure 1 shown, including the following steps:

[0049] S101. Acquire physiological monitoring data of the target object.

[0050]During specific implementation, the physiological monitoring data of the target object can be obtained through visits, hospital records, equipment monitoring and collection, and online sorting. The physiological monitoring data can include the basic information of the target object (such as age, gender, number of pregnancies, etc.), living habits Information (smoking frequency, drinking frequency, diet preference, mood state, sleep status, exercise frequency, work pressure, etc.), physical sign data (height, weight, waist circumference, hip circumference, etc.) and physical examination data (with or without high blood pressure, triglycerides , high and low-density lipoprotein, etc.), the risk prediction results obtained in this way are more accurate, and have guiding signifi...

Embodiment 2

[0079] This embodiment provides a diabetes risk prediction device, such as Figure 6 shown, including:

[0080] an acquisition unit, configured to acquire physiological monitoring data of the target object;

[0081] The generation unit is used to perform data cleaning on the physiological monitoring data of the target object, and perform data conversion on the cleaned physiological monitoring data to generate numerical data;

[0082] The prediction unit is used to import the numerical data into the trained diabetes risk prediction model for processing to obtain the predicted value;

[0083] The calling unit is used to call a corresponding forecast display file from the database for display according to the forecast value.

Embodiment 3

[0085] This embodiment provides a computer device, such as Figure 7 As shown, at the hardware level, including:

[0086] memory for storing instructions;

[0087] A processor, configured to read the instructions stored in the memory, and execute the diabetes risk prediction method described in Embodiment 1 according to the instructions.

[0088] Optionally, the computer device also includes an internal bus and a communication interface. The processor, the memory and the communication interface can be connected to each other through an internal bus, which can be an ISA (Industry Standard Architecture, industry standard architecture) bus, a PCI (Peripheral Component Interconnect, peripheral component interconnection standard) bus or an EISA (Extended Industry Standard Architecture, extended industry standard architecture) bus, etc. The bus can be divided into address bus, data bus, control bus and so on.

[0089]The memory may include, but is not limited to, random access m...

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Abstract

The invention relates to the technical field of machine learning, in particular to a diabetes risk prediction method, a device, equipment and a storage medium. The method comprises the steps of obtaining physiological monitoring data of a target object; performing data cleaning on the physiological monitoring data of the target object, and performing data conversion on the cleaned physiological monitoring data to generate numerical data; importing the numerical data into the trained diabetes risk prediction model for processing to obtain a prediction numerical value; and calling a corresponding prediction display file from a database according to the prediction value for display. According to the method, whether the illness risk exists or not can be predicted in advance according to the physiological omen of diabetes, the risk prediction efficiency and accuracy are improved, and corresponding instructive suggestions are provided.

Description

technical field [0001] The present invention relates to the technical field of machine learning, in particular to a diabetes risk prediction method, device, equipment and storage medium. Background technique [0002] Diabetes is a group of metabolic diseases characterized by hyperglycemia. With the development of social economy, the prevalence of diabetes is on the rise worldwide. Hyperglycemia is caused by defective insulin secretion or impaired biological action, or both. Long-term high blood sugar leads to chronic damage and dysfunction of various tissues, especially the eyes, kidneys, heart, blood vessels, and nerves. Therefore, the early prevention of diabetes plays a vital role in the treatment of diabetes. [0003] At present, diabetes mainly relies on invasive measurement to directly obtain test results, so as to carry out post-event targeted treatment after diagnosis and testing, instead of conducting a kind of advance and predictable physiological information su...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H50/30G16H50/20G06N3/04G06N3/08
CPCG16H50/30G16H50/20G06N3/084G06N3/048G06N3/045
Inventor 郜铮
Owner HEBEI UNIV OF ENG
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