High-performance detection of alcoholism by unfolding the amalgamated EEG spectra using the Random Forests method

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dc.contributor.authorRieg, Thilo
dc.contributor.authorFrick, Janek
dc.contributor.authorHitzler, Marius
dc.contributor.authorBuettner, Ricardo
dc.date.accessioned2019-01-03T00:19:19Z
dc.date.available2019-01-03T00:19:19Z
dc.date.issued2019-01-08
dc.description.abstractWe show that by unfolding the outdated EEG standard bandwidths in a fine-grade equidistant 99-point spectrum we can precisely detect alcoholism.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 consequences, alcohol consumption is always a critical issue in our society. Consequences of alcoholism in the brain can be recorded using electroencephalography (EEG). Our work can be used to automatically detect alcoholism in EEG mass data within milliseconds. In addition, our results challenge the medically outdated EEG standard bandwidths.
dc.format.extent9 pages
dc.identifier.doi10.24251/HICSS.2019.455
dc.identifier.isbn978-0-9981331-2-6
dc.identifier.urihttp://hdl.handle.net/10125/59813
dc.language.isoeng
dc.relation.ispartofProceedings of the 52nd Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectBig Data on Healthcare Application
dc.subjectInformation Technology in Healthcare
dc.subjectAlcoholism, Electroencephalography, Machine Learning, Random Forests, Spectral analysis
dc.titleHigh-performance detection of alcoholism by unfolding the amalgamated EEG spectra using the Random Forests method
dc.typeConference Paper
dc.type.dcmiText

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