Machine learning diabetes onset risk prediction method and application

Abstract

The invention provides a machine learning diabetes 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 for the purpose of diabetes risk prediction; and a display output module for testing the obtained to-be-predicted sample and outputting a prediction result. If the prediction result is 1, the diabetes risk exists, and if the prediction result is 0, the diabetes risk does not exist. According to the embodiment of the invention, the diabetes risk prediction model is constructed by combining metabonomics characteristics based on 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 adopting a novel predictor to predict the risk of diabetes onset. Background technique [0002] Disease risk prediction is mainly used to assist clinical decision-making, to assist the health detection of sensitive groups, and to detect disease risk for non-medical personnel. [0003] Type Ⅱ diabetes is a common metabolic disease in the department of endocrinology, accounting for more than 90% of diabetic patients. At present, the prediction of diabetes risk mainly includes two prediction methods based on the judgment of medical staff based on professional knowledge and the simple prediction model constructed by using traditional risk factors. Judging diseases through medical staff's experience and self-learning is the main clinical method at present. However, there are problems such as large subjective differences and low efficiency in medical staff's...

AI Technical Summary

Problems solved by technology

[0004] The invention overcomes the problems of low efficiency of risk prediction for medical personnel, non-uniform standards, subjective differences, low predictive ability of simple prediction models, and poor individual pertinence of traditional predictive factor construction models

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