Machine learning diabetic retinopathy onset risk prediction method and application

Abstract

The invention provides a machine learning diabetic retinopathy onset risk prediction method. The method comprises the following steps: a data acquisition module for acquiring metabonomics data; a data preprocessing module for preprocessing the acquired data; a machine learning module for constructing a prediction model based on a machine learning algorithm and metabonomics data in order to predict the risk of diabetic retinopathy; a display output module for testing the obtained to-be-predicted sample and outputting a prediction result, wherein if the prediction result is 1, the diabetic retinopathy risk exists, and if the prediction result is 0, the diabetic retinopathy risk does not exist. By applying the embodiment of the invention, the diabetic retinopathy risk prediction model is constructed by combining metabonomics characteristics on the basis of technologies mainly based on the random forest and the support vector machine algorithm. The method can be used for improving decision-making efficiency, guiding non-medical personnel to carry out disease risk detection or assisting clinical decision-making, and achieving the purposes of three-level prevention of diseases and promotion and development of health of the whole people.

Description

technical field [0001] The invention belongs to a method for constructing a model by using a machine learning algorithm and using a novel predictor to predict the risk of diabetic retinopathy. Background technique [0002] Disease risk prediction is mainly used to assist clinical decision-making, health detection of sensitive groups, and disease risk detection for non-medical personnel. [0003] Diabetes mellitus is a group of metabolic diseases characterized by hyperglycemia and caused by multiple etiologies. Diabetic retinopathy is one of the most common microvascular complications of diabetic patients, and it is also the main cause of blindness in patients, which increases the disease and economic burden of individuals and society. [0004] At present, the prediction of the risk of diabetic retinopathy mainly includes relying on the professional knowledge of medical staff or using simple prediction models constructed by traditional risk factors. Judging diseases through...

AI Technical Summary

Problems solved by technology

[0005] The invention overcomes the problems of low efficiency of risk prediction for medical personnel, non-uniform standards, subjective differences, low predictive ability of simple predictive models, poor individual pertinence of traditional predictive factor construction models, etc.

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