Patent ID: 11875878
Assignee: THE CATHOLIC UNIVERSITY OF KOREA INDUSTRY-ACADEMIC COOPERATION FOUNDATION
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
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.