Diabetes key characteristic parameter acquisition method

A key feature and parameter acquisition technology, which is applied in health index calculation, medical automated diagnosis, medical informatics, etc., can solve problems such as poor adaptability of methods, large influence of samples, and restriction of prediction accuracy, so as to reduce dimensions, improve prediction accuracy, The effect of increasing complexity

Pending Publication Date: 2022-07-29
LINGNAN NORMAL UNIV +1
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Problems solved by technology

[0005] Regression statistical prediction methods are usually aimed at specific groups of people and are greatly affected by samples, so the adaptability of the method is poor; intelligent prediction methods are easy

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  • Diabetes key characteristic parameter acquisition method
  • Diabetes key characteristic parameter acquisition method
  • Diabetes key characteristic parameter acquisition method

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Embodiment 1

[0065] Due to the numerous influencing factors of diabetes, the difficulty in extracting the features of diabetes risk factors that predict their contribution, and the low accuracy of existing diabetes prediction methods, effective prediction cannot be achieved. By means of various feature selection methods, the present invention can extract key features of diabetes risk factors, provide high-quality training data for prediction models, and at the same time use random forest algorithm to construct prediction models, which can effectively improve diabetes prediction accuracy. Based on this, a method for obtaining key characteristic parameters of diabetes was proposed.

[0066] In order to verify the feasibility and effectiveness of the present invention, four groups of experiments were carried out, namely, the feature set analysis experiment obtained by different feature selection algorithms, the diabetes prediction model training and prediction experiment, the model feature con...

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Abstract

The invention discloses a diabetes key feature parameter acquisition method, which comprises the following steps of: performing algebraic combination and standardization processing on diabetes related original data to obtain a diabetes risk factor candidate feature set; the method comprises the following steps: screening features related to diabetes through an RReliefF algorithm, and constructing a maximum correlation feature set; redundant features irrelevant to diabetes mellitus are removed through an mRMR algorithm, and a maximum-correlation and minimum-redundancy feature set is constructed; and performing causal replacement by adopting an improved FCL causal discovery method to obtain a diabetes key feature set. By means of algebraic combination, the complexity of the diabetes feature set can be greatly increased, and the diabetes features with higher prediction contribution degree can be selected conveniently; a maximum-correlation minimum-redundancy diabetes feature set is obtained by using an RReliefF algorithm and an mRMR algorithm, and the dimensionality of the diabetes feature set is reduced; an improved FCL algorithm is used to carry out causal replacement before diabetes features, and a diabetes feature set with better contribution degree is obtained.

Description

technical field [0001] The invention relates to the technical field of feature extraction, in particular to a method for obtaining key feature parameters of diabetes. Background technique [0002] Diabetes has become an epidemic disease that seriously affects human health. How to effectively prevent and treat diabetes has become an urgent problem to be solved. Analyzing the relationship between risk factors and diabetes and establishing a diabetes prediction model is the key to revealing the pathogenesis of diabetes, and it is also an effective auxiliary means for diabetes prevention and treatment. The existing diabetes prediction methods can be mainly divided into two categories: regression statistics and intelligent prediction. [0003] (1) Diabetes prediction method based on regression statistics. There are mainly Logistic, Cox, risk factor scoring, ROC and other methods. Among them, the Logistic method has the advantages of simple sample requirements and convenient mo...

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Application Information

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IPC IPC(8): G16H50/30G16H50/20
CPCG16H50/30G16H50/20
Inventor 陈波高秀娥胡建刚陈世峰桑海涛蒋潘玲
Owner LINGNAN NORMAL UNIV
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