High-performance exclusion of schizophrenia using a novel machine learning method on EEG data

Using the Random Forest method, we developed a fast-high-performance classification model, which can exclude a potential schizophrenic disorder in a diagnosis of potentially exposed people. Our model mainly consists of three preprocessing steps: ICA, Spectral Analysis using Buettner et al.'s 99-frequency-band-method and normalization. Using this preprocessing pipeline followed by a Random Forest, validated with different parameters, random states and a 10-fold-cross-validation, we could exclude schizophrenia with an accuracy of 100%. By applying this model in combination with a differential diagnoses system, treatments in ICUs can be done much faster, more accurately and be less expensive.