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

Date
2019-01-08
Authors
Rieg, Thilo
Frick, Janek
Hitzler, Marius
Buettner, Ricardo
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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We show that by unfolding the outdated EEG standard bandwidths in a fine-grade equidistant 99-point spectrum we can precisely detect alcoholism.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 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 In addition, our results challenge the medically outdated EEG standard bandwidths.
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Big Data on Healthcare Application, Information Technology in Healthcare, Alcoholism, Electroencephalography, Machine Learning, Random Forests, Spectral analysis
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