Type 2 diabetes prediction method and system based on human body composition

Abstract

The invention discloses a type 2 diabetes prediction method and system based on the human body composition. The method comprises the following steps: acquiring basic information of a patient and related data of the human body composition; taking whether the follow-up visited patient suffers from type 2 diabetes or not as a dependent variable, taking the obtained factors as independent variables to carry out single-factor analysis, and screening factors with statistical difference according to the P value of each factor; carrying out multi-factor Logistic regression analysis on independent variables with statistical differences in a single-factor analysis result, selecting a model with the best fitting degree according to AIC values, determining a regression coefficient which is finally incorporated into the independent variables, and representing the regression coefficient by adopting a Nomogram column graph; and predicting the onset risk of the type 2 diabetes of the patient according to the predicted cut-off value of the column graph.

Description

technical field [0001] The invention relates to the technical field of diabetes prediction, in particular to a type 2 diabetes prediction method and system based on body composition. Background technique [0002] Diabetes is recognized as a major public health problem worldwide. In recent years, the incidence of type 2 diabetes has increased year by year, and the occurrence of type 2 diabetes and its complications has brought a significant economic burden to patients and is the main cause of death. [0003] Among diabetic patients, a large proportion of patients are undiagnosed (such as asymptomatic), but are still at high risk of serious complications such as cardiovascular disease, kidney damage, retinopathy, etc. Several intervention studies have found that for People at high risk of diabetes can prevent and delay the occurrence of type 2 diabetes through lifestyle improvement and drug intervention. In this context, it is particularly important to use simple and effecti...

AI Technical Summary

Problems solved by technology

However, due to differences in race, demographics, and living habits, these models may not be suitable for other populations

Most of the models currently studied are based on demographic indicators (age, gender, family history of diabetes, etc.), lifestyle (smoking, drinking, eating habits, etc.), anthropometric measurements (height, weight, waist circumference, waist-to-hip ratio, systolic blood pressure, Diastolic blood pressure, etc.), clinical test indicators (triglyceride, cholesterol, high-density cholesterol, low-density cholesterol, fasting blood, etc.), most models include clinical test indicators, which may reduce the patient’s voluntary sex

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