Method and system for constructing cardiovascular disease diagnostic model, and diagnostic model thereof

Abstract

The invention discloses a method for constructing a cardiovascular disease diagnosis model, a system and the diagnosis model. The cascade classifier is trained to get the ear detection model. VGG, GoogleNet and ResNet neural network models are used to extract ear features. The spatial pyramid is used to integrate the features of ear extracted from neural network model, and the depth heterogeneousfeature map of each neural network model is obtained. Feature preprocessing of depth isomerism feature map; Training to obtain SVM classifier model; The SVM classifier model and the three trained neural network models are integrated by Bagging learning to obtain the cardiovascular disease diagnosis model. The cardiovascular disease diagnosis model constructed by the invention can comprehensively and scientifically carry out cardiovascular disease diagnosis and prediction, has high precision, and can be widely applied in the field of automatic processing of medical data.

Description

technical field [0001] The invention relates to the technical field of computer software, in particular to a method and system for constructing a cardiovascular disease diagnosis model and the diagnosis model. Background technique [0002] Most of the existing artificial intelligence models for cardiovascular disease detection are based on X-ray photos or electrocardiograms of the patient's heart. Obtaining these pictures requires a relatively cumbersome process and the support of a large number of professional equipment, and High cost and time-consuming. The existing schemes for disease diagnosis through face photos usually only extract the features of certain areas of the face, such as forehead, nose, etc., and then use traditional machine learning algorithms to classify, so as to realize diagnosis based on the classification results. The relationship between the characteristics of these locations and specific diseases lacks scientific basis, so the accuracy rate is low. ...

AI Technical Summary

Problems solved by technology

Traditional technology uses machine learning methods to extract ear features, but the features extracted by traditional machine learning methods are relatively single, usually only simple features such as color and texture can be extracted, and the extraction of these features lacks guidance, and the final features are often different from the Cardiovascular disease association is weak, so the diagnosis of cardiovascular disease cannot be made accurately

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