197 results about "Disease classification" patented technology

The invention discloses a multi-modal medical image data intelligent processing system, relates to the field of medical image analysis, and is applied to multi-modal medical image whole-process analysis of CT, MRI, PET, ultrasound and the like. According to the system, different modal image features are extracted and fused through a cross-modal manifold fusion network; a semantic guidance dynamic registration engine optimizes registration parameters to ensure that the registration error is less than or equal to 1.5 mm; the multi-task collaborative diagnosis network realizes multiple tasks such as disease classification; the clinical knowledge embedding and interpretable module generates a structured report and is in butt joint with an HIS system. Meanwhile, the model is optimized through a federated learning architecture, the adaptability of newly added data is improved by more than or equal to 20%, and intelligent processing and analysis of multi-modal medical images are realized.

The invention relates to the field of image analysis, in particular to a temporal bone disease classification method and system based on a multi-modal medical image fusion technology. The method comprises the following steps: acquiring a multi-modal image of a patient, performing adaptive distortion correction, and generating a standardized image set; performing layer-by-layer anatomical structure semantic segmentation and multi-modal image fusion on the standardized image set to construct an image fusion framework; according to the image fusion framework, performing intelligent recognition on the fine structure of the temporal bone, and constructing a personalized temporal bone anatomical structure chart; performing tissue function state analysis and digital pathology dynamic simulation based on the personalized temporal bone anatomical structure chart, and constructing a digital pathology model; and performing intelligent pathological feature classification based on the digital pathological model to obtain an intelligent classification report. According to the method, rapid, efficient and accurate temporal bone disease classification is realized.

The invention belongs to the related technical field of brain image processing, and provides an Alzheimer's disease classification method and system based on topology perception and a group hypergraph in order to solve the problem of inaccurate classification of the Alzheimer's disease in the prior art. Constructing a dynamic function connection network sequence through a sliding window strategy; a local topology perception encoder and a global topology perception encoder are respectively used for extracting local topology features and global topology features of each time window, deep interaction and fusion are carried out, and comprehensive feature representation of a tested level is generated; according to the method, each subject is used as a hypergraph node, hyperedges are constructed on the basis of comprehensive feature representation of a subject level and by combining feature similarity calculated by diffusion tensor imaging features and clinical embedded features of the subject, then a group hypergraph is constructed, a classification result is obtained by using a hypergraph neural network, and the early classification diagnosis accuracy of the Alzheimer's disease is effectively improved.

The invention discloses a medical data processing method, system and device based on big data and a medium. The method comprises the steps that medical image data and medical text data of a patient are acquired; performing feature extraction on the medical image data to obtain an image feature vector, and analyzing the medical text data to obtain a text feature matrix; inputting the image feature vector and the text feature matrix into a multi-modal fusion model, calculating an association weight of the image feature and the text feature, and obtaining a fusion feature representation; and establishing a disease prediction model according to the fusion feature representation, performing disease classification diagnosis on the patient, and generating a structured diagnosis report according to a diagnosis result. According to the method, the medical image data and the text data are deeply fused through the multi-modal fusion model, the complementary advantages of different modal data are fully utilized, and the problem of information missing possibly existing in a single data source is avoided.

The invention relates to a bridge superstructure safety risk assessment method and system and a storage medium. The method comprises the following steps: acquiring disease score deduction information of a technical condition of a bridge superstructure; obtaining bridge superstructure technical condition scoring data of the sample bridge, bridge superstructure score-deducted indexes and disease classification categories of the score-deducted indexes, and analyzing common indexes in the score-deducted indexes according to the score-deducted indexes; calculating a deduction value of the disease classification category of the common index at each scale based on an LSHADE algorithm, and calculating a safety risk score of the bridge superstructure of the sample bridge; dividing safety risk levels; constructing a bridge safety risk evaluation model by adopting a CatBoost ensemble learning method, and training the bridge safety risk evaluation model by utilizing a frequency reduction coefficient and the like of a sample bridge; and evaluating the safety risk level of the bridge superstructure by using the trained bridge safety risk evaluation model. The method is high in evaluation efficiency, low in cost and high in accuracy.

The invention provides a diagnosis and treatment model training method and system and electronic equipment, and relates to the technical field of medical diagnosis and treatment. The method comprises the following steps: performing field pre-training on a basic large language model by utilizing a medical corpus to obtain a pre-trained diagnosis and treatment model; corresponding diagnosis reasoning process information is generated for each piece of target medical record data in the target medical record data set, a supervision fine tuning data pair set is obtained, and each supervision fine tuning data pair in the supervision fine tuning data pair set comprises medical record information and a diagnosis conclusion containing the diagnosis reasoning process information; and performing supervision fine tuning training on the pre-trained diagnosis and treatment model by using the supervision fine tuning data pair set to obtain a trained diagnosis and treatment model. According to the method, the diagnosis and treatment model can master professional contents such as disease classification, clinical symptoms, diagnosis processes and drug treatment, and the effect of simulating a doctor to gradually analyze the symptoms to obtain a diagnosis result is achieved, so that the accuracy of the diagnosis and treatment result is improved.

The invention discloses a multi-omics data integration and classification method, system and device based on hierarchical attention, and is applied to the field of precise medical big data analysis. The method comprises the following steps: firstly, generating feature embedding and feature importance scores through a plurality of parallel feature-level attention modules; then, embedding and inputting all the characteristics of the omics into a unified omics-level attention module, and generating omics embedding and omics importance scores; and finally, a classification prediction task is executed based on omics embedding, and a classification result is output for disease classification. The invention completely abandons a traditional dependency graph convolutional network and an integration normal form of variants of the dependency graph convolutional network, and provides a universal hierarchical attention integration architecture. The framework supports classification tasks of any complex diseases, is not limited by omics data types and combination modes, not only is remarkably superior to a traditional integration normal form in classification performance, but also shows a unique negative generalization distance, and proves that the framework has excellent generalization ability. Meanwhile, features and omics importance scores automatically output by the model provide a powerful analysis tool for biomarker discovery and precise diagnosis and treatment of complex diseases.

The invention particularly relates to a respiratory disease diagnosis device and method, and the device comprises a respiratory sound signal collection unit which is used for obtaining a current respiratory sound signal; the breathing sound signal preprocessing unit is used for performing segmentation processing and denoising processing on the current breathing sound signal to obtain a preprocessed breathing sound signal; the feature extraction unit is used for extracting multi-modal features of the preprocessed breath sound signals; and the detection unit is used for inputting the multi-modal features into a preset deep learning network model to obtain a respiratory system disease detection result. Therefore, through multi-modal feature extraction, dynamic fusion and an efficient classification strategy, the problems of relatively low feature identification degree, relatively low disease classification accuracy and the like in the process of researching disease diagnosis based on the breath sound signals in related technologies are solved, and the detection performance of chronic respiratory system diseases based on the breath sound signals is remarkably improved.

The invention discloses an Alzheimer's disease diagnosis classification method based on a multi-modal nerve image, which is used for auxiliary diagnosis of Alzheimer's disease. The method comprises the following steps: firstly, respectively extracting image features of sMRI and PET brain images by using a self-attention vision converter, extracting global information, improving the feature discrimination capability, and enabling a model to capture a remote dependency relationship more easily, and secondly, realizing complementary fusion of the two image features by using an interactive attention fusion network. And finally, using a Stacking ensemble learning framework as a classifier. According to the method provided by the invention, the pathological information of sMRI and PET brain images is combined, the Alzheimer's disease classification accuracy is improved, a doctor can be assisted in diagnosis, and the method has a wide application prospect in the field of computer-aided diagnosis.

The invention discloses a construction method of an Alzheimer's disease classification model based on multi-modal feature fusion, and relates to the field of medical image processing. The problems that in the prior art, an Alzheimer's disease diagnosis method based on deep learning is insufficient in multi-modal data utilization, high in model complexity, high in medical cost and the like are solved. According to the method, sMRI images and clinical scale data serve as input data, the 3D MLP-Mixer obtains image detail information and space information, the GRU is used for extracting behavior cognition information, the ACF module is used for fusing the behavior cognition information and the image information extracted by the 3D MLP-Mixer, and therefore the patient recognition capacity and the cross-dataset generalization performance of the model are improved. The method is also suitable for the field of Alzheimer's disease image processing.

The invention relates to the technical field of digital medical treatment, and particularly discloses an artificial intelligence diagnosis system for nervous system diseases. The system comprises a data acquisition module, a preprocessing module, a multi-modal feature extraction module, a feature fusion module, a disease classification module, an interpretability analysis module and a doctor feedback and model updating module. The system supports acquisition and fusion of medical images, electroencephalogram signals, biochemical indexes, questionnaire information and wearable device data, constructs unified feature representation through a weighted attention mechanism, and outputs a prediction result of nervous system diseases based on a deep learning model. And meanwhile, the system is combined with a Shapley value method to realize the interpretability of the diagnosis process, and supports closed-loop return of doctor feedback information and model increment updating. The nervous system disease diagnosis accuracy and early recognition capability are improved, and the method has relatively high clinical deployability and adaptive optimization capability.

The invention relates to a fig health status assessment method and system based on multi-modal data fusion, and the method comprises the steps: constructing a multi-modal feature vector through fusing the features of a multi-spectral image, a hyperspectral cube and a visible light image; then proposing a multi-modal fusion suitability evaluation model, pre-processing the multi-modal feature vector, and then carrying out deep fusion and feature association to obtain a fusion feature; and finally, according to the fusion features, outputting an adaptation score and disease and pest classification. Therefore, the characteristic representation integrity is obviously enhanced, the evaluation accuracy and the pest and disease classification precision are improved, and the robustness and the anti-interference capability are enhanced.

The invention discloses a bridge apparent disease recognition method, which comprises the following steps of: 1, acquiring a two-dimensional image sample of a bridge apparent disease, marking a disease type according to a preset disease classification standard, and constructing an initial image data set; step 2, labeling the initial image data set to generate a label data set, and dividing the label data set into a training set, a verification set and a test set according to a ratio of 7: 2: 1; step 3, constructing a CSW-YOLO v9 model on the basis of a YOLOv9-m model architecture; 4, configuring hyper-parameters of the CSW-YOLO v9 model, performing iterative training by using the training set, performing performance verification through the verification set, and finally generating an optimized weight file; and step 5, inputting a to-be-detected bridge image into the CSW-YOLO v9 model loaded with the weight file, and outputting an apparent disease type and position information. The bridge apparent disease identification method provided by the invention is suitable for intelligent detection work of bridge multi-disease and small-target feature tasks.

The invention relates to the technical field of artificial intelligence, in particular to a special child disease classification method based on particle-ball cross-granularity knowledge collaborative feature selection. The method comprises the following steps: firstly, providing a cross-granularity knowledge collaboration method based on granules and balls; further designing a fuzzy rough set model based on particle-ball cross-granularity knowledge collaboration to perform feature selection; according to the method, feature knowledge under different granularity levels is mined and coordinated to realize more robust and more efficient feature selection, so that the accuracy, generalization ability and interpretability of a classification model are improved, and a more reliable technical tool is provided for early screening and auxiliary diagnosis of diseases of special children.

The invention relates to a multi-modal multi-task fusion attention model for Alzheimer's disease classification, and belongs to the technical field of attention model construction, and the multi-modal multi-task fusion attention model comprises a feature extraction module, a clinical guidance attention module, a proxy attention mechanism module, a dynamic gating feature fusion module and a multi-task loss function module which are connected in sequence. The agent attention mechanism module is also connected with the deep cross network module and the contrast learning projection module. According to the model, efficient fusion of image-clinical data is realized through a clinical attention guiding module, a dynamic gating feature fusion module and a multi-task comparison decoupling three-stage collaborative mechanism. The multi-modal multi-task fusion attention model is trained and verified based on 912 multi-center data of an ADNI database, the AD / CN binary classification accuracy is 100%, the AD / MCI binary classification accuracy is 95.16%, the AD / CN / MCI ternary classification accuracy is 92.47%, and the advanced level is achieved.

The invention provides a mental disorder aided decision-making method and system based on multi-modal data, and belongs to the technical field of disease aided decision-making, the method is applied to a system comprising a data acquisition module, a preliminary screening module and an aided decision-making module, and the method specifically comprises the following steps: preprocessing the multi-modal data of a patient; in combination with the mental disorder risk level of the patient of the preliminary screening model, decision assistance is triggered according to the mental disorder risk level, or corresponding decision suggestions are matched and output; when decision assistance is triggered, the disease classification probability and severity are obtained through a diagnosis model based on a cross-modal attention mechanism, meanwhile, according to a time-dependent risk prediction model, a survival probability curve of a recurrence risk is generated in combination with historical diagnosis data of a patient, and decision assistance suggestions are determined by integrating outputs of the two models. According to the method, on the basis of multi-modal data, multiple types of intelligent models are fused, objective data support is provided in the assessment and intervention process, and the missed diagnosis and misdiagnosis risks of mental disorders are reduced.

The invention provides an Alzheimer's disease prediction method based on incomplete modal comparative learning, and belongs to the technical field of multi-modal medical data classification. According to the method, an Alzheimer's disease prediction model is trained based on an Alzheimer's disease Obtaining an Alzheimer's disease classification result by using the trained Alzheimer's disease prediction model; the Alzheimer's disease prediction model comprises the following steps: extracting three-dimensional magnetic resonance imaging features and three-dimensional PET image features by using Swin Transform; obtaining structured data features based on the demographic data; splicing the three-dimensional magnetic resonance imaging features, the three-dimensional PET image features and the structured data features to obtain a comprehensive feature vector; and based on the comprehensive feature vector, carrying out classification through Transform to obtain an Alzheimer's disease classification result. Extracting spatial structure features and structured attribute information through a feature extractor; the similarity relation of the positive and negative sample pairs in a geometric space is optimized through a geometric contrast loss module, and the feature robustness and the discrimination capability under the condition of mode deficiency are improved.

The invention discloses a multi-classification risk prediction method and system for liver diseases based on machine learning, and relates to the technical field of biomedicine, and the method comprises the following steps: collecting fatty liver disease diagnosis results and biochemical indexes of a subject to form a training sample set, the method comprises the following steps: screening out biochemical indexes significantly related to fatty liver diseases through single-factor regression analysis, determining potential risk factors, carrying out multicollinearity test on the factors, screening out risk factors, constructing a plurality of machine learning classification models for training, and selecting a model with the best performance as a reference model. The contribution degree of each important risk factor is evaluated and sorted, classification significant factors are determined, the significant factors serve as classification metadata, a plurality of judgment models are trained, input factors are dynamically selected according to contribution values, and finally a fatty liver disease degree classification result is output, so that complex conditions under different sample features and clinical backgrounds are better handled; and the accuracy of prediction results is improved.

This invention relates to a deep learning-based method for multi-angle feature extraction and selection of magnetocardiogram (MCG) signals. The method includes: preprocessing the MCG signal; designing a temporal feature extraction method based on learnable positional encoding and a CNN-Transformer deep learning model; a frequency domain feature extraction method based on HHT and a CNN deep learning model; and a multi-modal feature extraction method based on adaptive VMD and center frequency weighting. A feature selection model based on adaptive LASSO is used to select the subset of MCG features that has the greatest impact on disease classification. Compared with traditional MCG feature extraction and selection methods, the proposed feature extraction and selection method can automatically and more accurately extract multi-angle MCG features, and the extracted features have more angles, better robustness, higher reliability, and a wider range of applicable disease classifications.

The invention provides a mental disease classification method and system based on an individual difference structure covariant network and machine learning, and belongs to the field of mental disease classification. The problem of classification performance bottleneck caused by heterogeneity of mental diseases in the prior art is solved. The method comprises the following steps: acquiring a structural magnetic resonance T1 weighted image, and preprocessing the image; performing brain region segmentation on the pre-processed T1 image based on an AAL brain map, and extracting the gray matter volume of each brain region; constructing an IDSCN network by calculating the Pearson's correlation coefficient of the brain grey matter volume of the paired brain regions; calculating the area under a node topological attribute curve of the IDSCN network; screening node attribute indexes with statistical differences between the patient group and the healthy control group through double-sample t test; and taking the screened node attribute indexes as feature vectors, and inputting the feature vectors into a support vector machine classification model for disease classification. The method is mainly used in the medical image processing field.

The invention discloses a method for constructing a diagnostic model for identifying leaf diseases. The method comprises the following steps: constructing a leaf disease original image set; preprocessing the leaf disease original image set to generate a leaf disease image sample set; the content of the leaf disease image sample set comprises leaf disease classification, leaf disease stages and a corresponding image set; the method comprises the following steps: defining a network model of which the basic structure is ResNet50, and adding an IB module to form a ResNet50-FIB structure; the IB module is used for performing multi-scale fusion and enhancement processing and outputting deep semantic features and spatial detail information, the deep semantic features are used for matching leaf disease classification, and the spatial detail information is used for matching leaf disease stages; the output layer outputs disease classification and disease stages according to image recognition; and training the network model, and constructing and generating a diagnosis model for identifying leaf diseases. According to the technical scheme, the bottlenecks of a traditional model in precision and speed balance, early disease misjudgment and cross-crop adaptability can be broken through, and the method has industrial popularization potential.

The invention discloses an intelligent evaluation method for screening multiple diseases of lumbar vertebra based on deep learning, and relates to the technical field of intelligent screening of lumbar vertebra diseases, and the method comprises the steps: collecting multi-source data, and obtaining lumbar vertebra CT, MRI, bone mineral density images, clinical diagnosis and treatment data and operation related parameters; preprocessing data, denoising images, segmenting key structures, and standardizing non-image data; a deep learning model is constructed, image features are extracted through CNN branches, non-image features are processed through Transform branches, and disease classification, severity assessment and risk prediction are achieved in combination with multi-task learning; integrating the multi-modal features through a feature fusion optimization algorithm, and outputting an evaluation result; doctor interaction correction and model iteration are supported, and the generalization ability of the system is improved. According to the method, multi-source data are integrated, the accuracy and efficiency of lumbar vertebra multi-disease assessment are improved, postoperative risk prediction is optimized, clinical diversified requirements are met, and powerful support is provided for precise diagnosis and treatment and rehabilitation.

The invention provides an auxiliary detection system for diagnosing Alzheimer's disease, which relates to the technical field of classification diagnosis of Alzheimer's disease, and comprises a data acquisition module, a data processing module, a feature extraction module, a diagnosis model module and a report generation module, according to the method, four types of core data including clinical information, scale scores, MRI images and blood biomarkers are integrated, the core dimension of Alzheimer's disease diagnosis is covered, so that the pathological evolution of AD from molecular abnormality to structural damage can be more comprehensively captured by utilizing collaborative analysis of multi-modal features, and the diagnosis accuracy of the Alzheimer's disease is improved. The problem of missed diagnosis or misdiagnosis caused by single modal data is avoided, and the diagnosis accuracy is improved. And secondly, aiming at the heterogeneity problem of image data and blood marker data, a standardized processing technology is adopted, so that the consistency of data of different sources and different batches is ensured, the influence of equipment difference and experimental error is eliminated, and a reliable guarantee is provided for model input.

The invention provides a method, system and device for vocal cord disease classification based on a ResNet model of MFCC, a medium and a program product, and relates to the field of vocal cord disease classification. The method comprises the following steps: S1) collecting pronunciation of a subject, constructing a corresponding label, and preprocessing the pronunciation of the subject; s2) converting the standardized pronunciation data set into a three-dimensional Mel spectrum feature map; s3) a vocal cord disease classification model is constructed, the vocal cord disease classification model adopts a ResNet architecture, the three-dimensional Mel spectrum feature map is used as input, and an SE module is embedded in each level of residual unit of the ResNet; s4) using the loss function as supervision of the vocal cord disease classification model to obtain a trained vocal cord disease classification model; and S5) inputting the pronunciation of the to-be-classified testee into the vocal cord disease classification model.According to the method, through organic combination of convolution kernel design, attention mechanism embedding and a lightweight channel expansion strategy, efficient and accurate recognition of pathological voice is realized.

The application discloses a kind of Zhang Xizheng pure Chinese medicine theory prescription medicine digital inheritance and intelligent analysis system.The system includes disease and syndrome differentiation rule engine subsystem, prescription three-way search subsystem, drug knowledge enhancement subsystem, medical record intelligent search subsystem, Zhang Xizheng school review subsystem and large language model interaction subsystem.The method performs the following steps: LLM clinical information extraction and disease prediction (extract symptoms, tongue, pulse, and 8 big disease classification prediction), disease and syndrome differentiation KB rule matching (8 big disease classification x multiple syndrome type weighted scoring matching), multi-source search (prescription three-way search+drug knowledge double-way vector recall and prescription-drug dynamic association+medical record multidimensional mixed scoring search), LLM fusion generation (inject all search results Zhang Xizheng school special prompt word generation theory prescription medicine analysis report) and Zhang Xizheng school four-dimensional review (western medicine perspective+air theory+drug use principles+theory prescription medicine consistency).The present application first realizes the computer formalization expression of Zhang Xizheng "disease differentiation before syndrome differentiation" mode and "air monism" theory, based on "Medical Western Medicine Record" 172 prescriptions+213 drugs / medical theory / medical conversation+137 cases to build a complete knowledge search and intelligent analysis system, with the ability of western medicine perspective+air theory+drug use principles+theory prescription medicine consistency.

The present invention relates to a diagnosis assistance device and method based on artificial intelligence processing of a radiographic image, wherein a disease diagnosis assistance device according to an embodiment of the present invention may comprise an information providing unit that constructs a diagnosis assistance model by learning a training radiographic image and additional information through a first feature processing unit, a second feature processing unit, and a feature fusion processing unit, determines a bone mineral density value and disease classification information with respect to a radiographic image to be diagnosed on the basis of the diagnosis assistance model, and provides disease diagnosis assistance information including the bone mineral density value, the disease classification information, and diagnosis basis information.

The application provides a medical task prediction model construction method, which comprises the following steps: S1, acquiring a training data set, wherein the training data set comprises time series blood pressure data in multiple preset time periods; S2, constructing an initial model, wherein the initial model comprises a feature extraction module, a multi-channel prototype network module and a full connection layer; S3, taking the time series blood pressure data as input, taking the disease classification result of the time series blood pressure data as prediction output, training the initial model according to a preset training rule until convergence, and obtaining a medical task prediction model. The medical task prediction model can perform feature extraction and similarity calculation on the data in different channels of the time series blood pressure data, so as to sufficiently obtain the feature information of the data in different channels and improve the prediction accuracy of the model; and the time series blood pressure data can be matched with a global prototype, so that the decision-making process of the model can be intuitively understood.

The invention relates to the technical field of skin disease classification, in particular to an auxiliary skin disease classification method and system based on multi-modal medical data, and the method comprises the steps: extracting features of a plurality of types of first data, and obtaining a plurality of initial feature vectors; performing low-level fusion and high-level fusion according to the plurality of initial feature vectors to obtain a comprehensive feature vector, inputting the comprehensive feature vector into a classifier, and determining a predicted skin disease category; and generating a diagnosis report according to the predicted skin disease category. According to the invention, low-level fusion and high-level fusion are carried out according to the plurality of initial feature vectors to obtain the comprehensive feature vector, and the predicted skin disease category is determined by fusing the data of the plurality of categories of the skin diseases and inputting the comprehensive feature vector into the classifier, so that the accuracy of skin disease classification is improved.

The application discloses a medical data processing method and product based on multi-stage transfer learning and multi-modal data collaborative fusion, relates to the technical field of medical data processing and artificial intelligence, and adopts MedicalNet medical special pre-training weights to initialize a classification model backbone network; a three-stage progressive fine-tuning framework is constructed on the basis, field adaptive coarse classification fine-tuning and target task fine classification are performed, and clinical structured data is introduced in the third stage; after high-dimensional image features are reduced in dimension and low-dimensional clinical features are increased in dimension through an adaptive multi-branch multi-level MLP architecture, mid-term deep fusion is performed, the application can effectively mine the complex relationships such as complementation, correlation and cooperation of multi-modal heterogeneous data, improve the lung disease classification precision and model generalization capability, and significantly inhibit the small sample overfitting phenomenon.

The invention discloses an Alzheimer's disease classification prediction method and system. According to the method, firstly, a bimodal brain network diagram is constructed, brain region features serve as nodes, and edge features are constructed through single nucleotide polymorphism data and structural magnetic resonance imaging data; the method is characterized in that closed-loop information interaction between nodes and edges is realized through a mutual feedback backflow graph neural network: firstly, edge features are dynamically updated based on node similarity, and information transmission from the nodes to the edges is realized; carrying out aggregation propagation and key screening among edge features by utilizing a topological neighborhood relationship; and finally, returning the edge feature information subjected to cross-modal fusion to the nodes, and updating node features. According to the method, the limitation that an existing graph neural network only pays attention to edge-to-node one-way information flow is overcome, and by describing dynamic coupling of nodes and edges and complementation of multi-modal information on a connection layer, the capturing capacity of early and tiny pathological modes of the Alzheimer's disease is enhanced, so that the accuracy and interpretability of classification prediction are improved.