Diabetes risk prediction method, device, equipment and storage medium

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...

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