Alzheimer's disease detection method based on data space transformation

Abstract

The invention discloses an Alzheimer's disease detection method based on data space transformation, and the method comprises the following steps: 1, modeling brain functional magnetic resonance imaging (fMRI) data of all data sets, and extracting features from a data model; 2, performing feature selection on the features extracted in the previous step; 3, mapping the selected feature data of all the data sets into the same subspace by using a data space transformation method; 4, training a machine learning classification model by using the data cross validation in the subspace, and adjusting parameters to obtain an optimal computer-aided diagnosis (CAD) model. According to the method, the problem of inconsistent distribution of different data sets is solved, available training samples fordisease diagnosis are increased, and the conditions of low accuracy and insufficient generalization ability of an auxiliary diagnosis model due to insufficient sample size are relieved; meanwhile, theAD auxiliary diagnosis accuracy based on the fMRI data is greatly improved by using feature engineering and a machine learning algorithm.

Description

technical field [0001] The invention relates to the technical field of computer-aided diagnosis, in particular to a method for detecting Alzheimer's disease based on data space transformation. Background technique [0002] Computer-aided diagnosis (CAD) has been a hot topic in Alzheimer's disease (AD) research. At present, AD diagnosis mainly relies on the clinical manifestations of patients. In the early stage of the disease, patients may have no obvious cognitive impairment or only It manifests as mild memory impairment, which makes it difficult to distinguish patients from normal aging. Due to the lack of more specific indicators in the diagnosis of the disease, the early diagnosis rate is very low. Accurate and early diagnosis of the disease is very important for timely treatment and reducing the risk of patients, so early and accurate judgment of the disease degree using CAD system is very important for the prognosis and treatment of patients. Functional Magnetic Reso...

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Problems solved by technology

[0003] The purpose of the present invention is to provide a detection method for Alzheimer's disease based on data space transformation, which solves the problem of inconsistent distribution of different data sets through the data space transformation method, increases the available training samples for disease diagnosis, and alleviates the lack of sample size As a result, the accuracy of the auxiliary diagnosis model is low and the generalization ability is insufficient.

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