A Supervised Machine Learning Algorithm for Arrhythmia Analysis
نویسنده
چکیده
A new machine learning algorithm for the diagno sis of cardiac arrhythmia from standard lead ECG recordings is presented The algorithm is called VFI for Voting Feature Intervals VFI is a supervised and inductive learning algorithm for inducing classi cation knowledge from examples The input to VFI is a train ing set of records Each record contains clinical mea surements from ECG signals and some other infor mation such as sex age and weight along with the decision of an expert cardiologist The knowledge rep resentation is based on a recent technique called Feature Intervals where a concept is represented by the projec tions of the training cases on each feature separately Classi cation in VFI is based on a majority voting among the class predictions made by each feature sepa rately The comparison of the VFI algorithm indicates that it outperforms other standard algorithms such as Naive Bayesian and Nearest Neighbor classi ers
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