CPC G16B 40/00 (2019.02) [G06N 3/126 (2013.01); G16B 5/00 (2019.02)] | 15 Claims |
1. A machine learning method using steps feature selection based on a genetic algorithm, comprising:
defining a feature set including a plurality of features, wherein the defining of the feature set includes defining each of the plurality of features as a combination between an electroencephalogram occurrence location and an electroencephalogram frequency band for an electroencephalogram signal;
generating a plurality of feature combinations including n-dimensional features (n is a natural number) for the feature set;
independently constructing feature models for the plurality of feature combinations and calculating prediction accuracy for each of the feature models as a prediction result for a predetermined data set;
arranging the feature models according to the prediction accuracy to determine at least one feature model that satisfies a preset criterion;
determining a first feature from among features included in a corresponding feature set of the at least one feature model;
updating the feature set to include only the first feature and re-determining a feature model for a (n+1)-dimensional feature combination based on the updated feature set, and
diagnosing one or more pathological symptoms using the feature model for the (n+1)-dimensional feature combination,
wherein the calculating of each of the prediction accuracy includes calculating the prediction accuracy based on whether each of the feature models matches a predicted value and an actual value regarding the presence or absence of amyloid for the predetermined data set.
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