Intelligent system for stroke risk prediction

A risk prediction and intelligent system technology, applied in the field of medical imaging, can solve the problems of not doing too much innovation, not too much in-depth research on disease risk prediction, and achieve the effect of improving diagnostic efficiency and inclusiveness

Pending Publication Date: 2020-12-18
HAINAN UNIVERSITY +1
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Problems solved by technology

However, the current traditional medical imaging computer-aided diagnosis system is based on the convolutional neural network in deep learning to train the lesion identification model, and then use the model to identify and detect the lesion to d

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  • Intelligent system for stroke risk prediction
  • Intelligent system for stroke risk prediction
  • Intelligent system for stroke risk prediction

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Embodiment Construction

[0034] In order to make the content of the present invention more clear and understandable, the content of the present invention will be described in detail below in conjunction with specific embodiments and accompanying drawings.

[0035] The present invention builds a machine learning model for a large amount of historical image data, extracts highly representative image features from image images, and combines them with patient basic data and other data to form a feature library, and constructs an imaging model for stroke diagnosis and risk prediction. An intelligent system for stroke risk prediction is proposed. Moreover, the present invention considers introducing the deep convolutional network and random forest algorithm into the establishment process of the patient's stroke risk prediction and diagnosis model, and establishes a robust stroke risk prediction model by providing a supervised and efficient machine learning method.

[0036] figure 1 A system block diagram o...

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Abstract

The invention provides an intelligent system for stroke risk prediction. The system comprises a data input unit which is used for receiving input data and carrying out the preprocessing of the input data; wherein the input data comprises basic data and Doppler data for establishing a feature library; a model training unit which is used for establishing a prediction model by taking a random forestmodel as a classifier and taking the feature subset selected from a feature database and the corresponding label information as training samples; and a prediction unit which is used for collecting basic data and Doppler data of a patient to be tested and inputting the collected data into a trained model to obtain the risk probability of cerebral apoplexy of the patient to be tested.

Description

technical field [0001] The present invention relates to the field of medical imaging; specifically, the present invention relates to an intelligent system for stroke risk prediction based on a deep convolutional network combined with a random forest algorithm, which utilizes artificial intelligence technology for precise analysis of medical images. Background technique [0002] Stroke is an acute cerebrovascular disease. According to surveys, stroke has become the number one cause of death in China combined in urban and rural areas, and it is also the leading cause of disability among Chinese adults. Due to the characteristics of high morbidity, high mortality and high disability rate of stroke, the medical profession currently believes that prevention is the best measure, so the risk prediction of stroke is of great significance to ordinary patients. In modern clinical diagnosis and treatment, medical imaging technology plays an important role. The analysis of medical imag...

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Application Information

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IPC IPC(8): G06K9/62G06N3/04
CPCG06N3/045G06F18/24G06F18/214
Inventor 谢小峰景香香刘丽莉何珂侯尧刘伟
Owner HAINAN UNIVERSITY
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