Diabetes mellitus probability calculation method based on large data of diabetes mellitus system

A technology of probability calculation and diabetes, which is applied in the field of probability calculation, can solve the problems of insufficient prevention factors, lack of prediction function, and inaccurate prediction, etc.

A technology of probability calculation and diabetes, which is applied in the field of probability calculation, can solve the problems of insufficient prevention factors, lack of prediction function, and inaccurate prediction, etc.

CN104636631AActive Publication Date: 2015-05-20JIANGSU ZHONGKANG SOFTWARE
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  • Diabetes mellitus probability calculation method based on large data of diabetes mellitus system
  • Diabetes mellitus probability calculation method based on large data of diabetes mellitus system
  • Diabetes mellitus probability calculation method based on large data of diabetes mellitus system

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[0053] The present invention is specifically realized through the following technical solutions:

[0054] Such as figure 1 and figure 2 As shown, the present invention mainly designs a method for calculating the probability of diabetes based on the big data of the diabetes system for optimizing diabetes prevention and treatment technology. The method first acquires training samples from a professional diabetes management system, that is, screens out the characteristic attributes that affect diabetes. The C5.0 decision tree model preliminarily predicts diabetes, uses decision tree pruning to further optimize the characteristic attributes, and finally uses the naive Bayesian model to calculate the probability of possible diabetes outcomes to obtain more intuitive and accurate prediction results. The specific steps are as follows:

[0055] 1) Construct the diabetes C5.0 decision tree model, extract 70% of the diabetes system big data as the training sample S of the decision tr...

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Abstract

The invention discloses a diabetes mellitus probability calculation method based on large data of a diabetes mellitus system. The diabetes mellitus probability calculation method comprises the following steps: (1) constructing a diabetes mellitus decision tree model; (2) selecting an optimal branch variable of the decision tree model according to the information gain ratio Gains (Xi) of a training sample S; (3) post-pruning a decision tree from bottom to top; and (4) constructing a diabetes mellitus naive Bayes model, and obtaining the diabetes mellitus probability P (C1|y1.y2.y3.....ym) with C1 serving as an output variable on a rth node by utilizing a Bayes formula. According to the diabetes mellitus probability calculation method disclosed by the invention, a two-layer model method with the combination of the decision tree and the naive Bayes model is designed; by extracting the characteristic attribute of diabetes mellitus from the large data, whether the diabetes mellitus occurs or not is forecasted, the probability of the occurrence of the diabetes mellitus is further calculated, and the prevention and the forecast are combined and are relatively overall and accurate.

Description

technical field [0001] The present invention relates to a method for calculating probability, in particular to a method for calculating probability of diabetes based on big data of diabetes system. Background technique [0002] With the development of social economy and the rapid increase of risk factors such as population aging and lifestyle, the prevalence of diabetes is on the rise worldwide. According to the statistics of the World Health Organization: in 1985, there were 30 million people with diabetes in the world, 135 million people in 1995, 177 million people in 2000, and it is estimated that it will reach 300 million people by 2025; about 4 million people die of diabetes and diabetes every year. Diabetes-related diseases account for 9% of the world's deaths. Many diabetic patients cannot be identified early, and the diabetic population is tending to be younger. The prevention and treatment of diabetes has become an important technical issue at present. [0003] Th...

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Application Information

Patent Timeline
20 May 2015
Publication
CN104636631A
IPC
G06F19/00
Inventors
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