Sample imbalance-oriented multi-disease classifier design method

Abstract

The invention aims to overcome the defects in the prior art, and provides a sample imbalance-oriented multi-disease classifier design method, which comprises the following steps: dividing medical casedata into a plurality of case sample subsets according to disease categories, and performing a feature selection method of a disease association rule on each sample subset; selecting a feature vectorof the case sample subset, iteratively and randomly updating the adoption probability on the premise that the imbalance degree is an upper limit threshold value, equalizing the case sample subset, training a weak classifier of each sample subset, and calculating the classification effect of the weak classifier; and finally, determining whether the iterative generation of the multi-disease classifier is finished or not by judging whether the difference value of the macro-F1 meets an iterative convergence threshold or not.

Description

technical field [0001] The invention relates to the field of machine learning, in particular to unbalanced samples and integrated learning algorithms. Background technique [0002] Domestic machine learning models have also been gradually used in multi-disease classification methods, but in the medical field, it is difficult to directly construct multi-disease classification models for those medical cases with few training samples. And with the improvement of the diagnostic ability of the diagnostic model, the number of features it needs will continue to expand, and the imbalance of case samples will gradually increase, which will eventually cause the dimension disaster of the feature matrix, excessive calculation, low classification accuracy, and low training accuracy. Problems such as sample sparsity and overfitting will ultimately affect the classification quality of the classifier. [0003] In order to overcome the problem of unbalanced case samples in these medical fie...

AI Technical Summary

Problems solved by technology

Only by solving the problem of unbalanced data sets can the accuracy of small-sample disease prediction be improved and artificial intelligence more popular

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