High-performance detection of alcoholism by unfolding the amalgamated EEG spectra using the Random Forests method
Published: Jan 1, 2019
Abstract
We show that by unfolding the outdated EEG standard bandwidths in a fine-grade equidistant 99-point spectrum we can precisely detect alcoholism. Using this novel pre-processing step prior to entering a random forests classifier, our method substantially outperforms all previous results with a balanced accuracy of 97.4 percent. Our machine learning work contributes to healthcare and information systems. Due to its drastic and protracted...
Paper Details
Title
High-performance detection of alcoholism by unfolding the amalgamated EEG spectra using the Random Forests method
Published Date
Jan 1, 2019
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