Disease prediction system based on medical image algorithm and block chain

Abstract

The invention discloses a disease prediction system based on a medical image algorithm and a block chain. The disease prediction system comprises a disease prediction model construction module and a disease prediction module; the disease prediction model construction module comprises a block chain storage unit, a data processing unit and a model construction unit; disease types, historical diagnosis and treatment data and medical images of patients are stored in the block chain storage unit; and the data processing unit is used for processing the diagnosis and treatment data and the medical images of the patients so as to establish a sample set; and the model construction unit is used for constructing a disease prediction model based on a BP neural network; the disease prediction module is used for obtaining a predicted disease type of a to-be-predicted patient according to the constructed disease prediction model. According to the disease prediction system based on the medical image algorithm and the block chain of the invention, the BP neural network is applied to disease prediction, and the disease can be effectively predicted, so that the method has important significance in early discovery and early treatment and reduction of the death rate of the disease.

Description

technical field [0001] The invention relates to the technical field of medical image (image) processing and utilization, in particular to a disease prediction system based on medical image algorithms and blockchain. Background technique [0002] In recent years, with the widespread application of electronic medical record systems and medical imaging technologies in hospitals, a large amount of medical data has been generated, and disease prediction based on medical data has become one of the current research hotspots. Predicting diseases in advance is of great significance for early detection and early treatment, and reducing the mortality of diseases. Human-driven analysis is impractical due to the massive, high-dimensionality, and heterogeneity of medical datasets. Artificial intelligence based on machine learning algorithms is particularly suitable for coping with the challenge of "big data". Commonly used machine learning algorithms include time series forecasting, Mark...

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Problems solved by technology

However, when the BP neural network is applied to disease prediction, it will be difficult to determine the structure, so it is easy to fall into a local minimum point, and the recognition rate and reliability may not be ideal when making predictions.

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