Medical diagnosis auxiliary method and system
A technology for medical diagnosis and medical data, applied in medical automation diagnosis, neural learning methods, computer-aided medical procedures, etc., can solve the problems of single model and incomplete data source, and achieve the effect of improving the overall accuracy and solving the incomplete data source.
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Embodiment 1
[0061] Using the above scheme, if figure 1 , image 3 As shown, a variety of medical data are preprocessed and summarized, and a variety of classification models are selected from the classification model pool to classify the summarized data. The decision values of the various classification models are summarized for decision fusion, and the decision fusion adopts the voting method.
[0062] The main operation of using the voting method is as follows: first, establish a dictionary of disease categories to count the votes; then, read the decision values of each sub-model cyclically, use the dictionary to count the categories and votes, and sort the votes; then Next, the relative majority voting method is used to calculate the voting structure. When a certain category has the most votes, the algorithm will output this category as the final conclusion; One of these categories is randomly selected as the final conclusion.
Embodiment 2
[0064] Using the above scheme, if figure 2 , Figure 4 As shown, after preprocessing a variety of medical data, different classification models are used for classification; decision fusion adopts the weight method.
[0065] The main operation of using the weight method is: the category probability of each modal data after analysis is regarded as its decision value, and this is used as the input of the algorithm; first, the sub-model decision values need to be aligned according to the category, converted and spliced into a decision matrix ; Next, use the decision matrix to multiply the corresponding elements of the weight array, and sum the weighted probabilities of each category by column; finally, output the category with the highest probability as the final conclusion.
[0066] Example 3
[0067] Another realization of the integration of the above schemes is to obtain more accurate auxiliary diagnosis conclusions through the analysis of various clinical data generated...
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