SVM (Support Vector Machine) based Alzheimer's disease characteristic classification method and system

A technology of support vector machine and feature classification, applied in genetic models, genetic laws, computer components, etc., can solve the problem of Alzheimer's disease efficiency and classification effect need to be further improved, to improve the efficiency of feature extraction, easy to implement , to ensure the effect of classification performance

Inactive Publication Date: 2018-06-12
广州市大智网络科技有限公司
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Problems solved by technology

[0006] However, there is no report on the combination of genetic algorithm and support vector machine for feature extraction and classification

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  • SVM (Support Vector Machine) based Alzheimer's disease characteristic classification method and system
  • SVM (Support Vector Machine) based Alzheimer's disease characteristic classification method and system
  • SVM (Support Vector Machine) based Alzheimer's disease characteristic classification method and system

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Embodiment 1

[0098] Aiming at the problem that the existing technology fails to combine genetic algorithm and support vector machine method to extract and classify Alzheimer's disease features, the present invention proposes a support vector machine-based Alzheimer's disease feature classification scheme For the first time, the method of genetic algorithm and support vector machine is combined and used for feature extraction and classification of Alzheimer's disease. Through the improved genetic algorithm, the average classification accuracy is used as the fitness value to improve the feature extraction of Alzheimer's disease Efficiency, and at the same time, the classification performance of Alzheimer's disease is guaranteed through the support vector machine classifier, which is intuitive, easy to implement, strong in generalization ability, and has good recognition performance. The scheme can find the key features that affect the conversion of different stages of Alzheimer's disease in a...

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Abstract

The invention discloses an SVM based Alzheimer's disease characteristic classification method and system. The method comprises that magnetic resonance imaging data of the Alzheimer's disease is obtained; an improved genetic algorithm is used to carry out characteristic optimization searching on the obtained magnetic resonance imaging data to obtain key characteristics of the Alzheimer's disease; and an SVM classifier is used to classify data to be classified according to the extracted key characteristics, and a classification result of the Alzheimer's disease is obtained. The system comprisesa data obtaining module, a characteristic optimization searching module and a classification module. A device comprises a memory and a processor. The improved genetic algorithm takes the average classification accuracy as the fitness value to improve the feature extraction efficiency of the Alzheimer's disease, the SVM classifier ensures the classification performance of the Alzheimer's disease, and the method and system are visual and easy to realize, can be generalized effectively, and has a good identification performance. The method and device can be widely applied to the field of computeraided diagnosis.

Description

technical field [0001] The invention relates to the field of computer-aided diagnosis, in particular to a method and system for classifying Alzheimer's disease features based on a support vector machine. Background technique [0002] Alzheimer's disease (Alzheimer's Disease, AD) is an irreversible chronic neurodegenerative disease and a persistent high-level neurological dysfunction. The existing drug treatments for AD are very limited, but early and accurate detection and treatment can slow down the disease process. Mild cognitive impairment (Mild Cognitive Impairment, MCI) is a transitional stage between normal healthy people (Health Controllers, HC) and AD, and MCI patients are a high-risk population for AD. Studies at home and abroad have pointed out that the important pathological signs and biomarkers of AD can be measured by Magnetic Resonance Imaging (MRI). The method of extracting effective features from MRI to classify and identify the three stages of AD, MCI and ...

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Application Information

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IPC IPC(8): G16H50/20G06K9/62G06N3/12
CPCG06N3/126G06F18/2411G06F18/2413
Inventor 潘丹曾安黎建忠
Owner 广州市大智网络科技有限公司
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