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International Journal of Advances in Intelligent Informatics
ISSN : 24426571     EISSN : 25483161     DOI : 10.26555
Core Subject : Science,
International journal of advances in intelligent informatics (IJAIN) e-ISSN: 2442-6571 is a peer reviewed open-access journal published three times a year in English-language, provides scientists and engineers throughout the world for the exchange and dissemination of theoretical and practice-oriented papers dealing with advances in intelligent informatics. All the papers are refereed by two international reviewers, accepted papers will be available on line (free access), and no publication fee for authors.
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Articles 1 Documents
Search results for , issue "Vol 7, No 3 (2021): November 2021 (Issue in Progress)" : 1 Documents clear
A machine learning approach for the identification and classification of the schizophrenia disorder using EEG signals Chioson, Francheska B.; Tolentino, Jolo Gerard Miel F.; Baldovino, Renann G; Bugtai, Nilo T.
International Journal of Advances in Intelligent Informatics Vol 7, No 3 (2021): November 2021 (Issue in Progress)
Publisher : Universitas Ahmad Dahlan

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Abstract

Schizophrenia is a mental disorder that causes a person to hallucinate and lose touch of reality. The mental disorder is complex and difficult to assess, therefore, multiple tests are done to validate if a person has developed schizophrenia. Prolonged diagnosis may lead to debilitating effects on how a person thinks, feels or reacts. With this in mind, researchers are looking into the difference in brainwave patterns between schizophrenic and healthy patients. Brainwaves are measured using EEG electrodes which are placed across the surface of the head. EEG signals are known to fluctuate heavily when sensory receptors are stimulated. In one study, Roach et al. correlated the lack of N1 suppression in schizophrenic patients when exposed to auditory stimuli. The research aims to further the study of Roache et. Al by testing different machine learning algorithms and determining the model with the best accuracy and computational time. The paper utilizes the lack of N1 suppression to classify schizophrenic patients from healthy patients. Each patient is exposed to different conditions that prompt their auditory receptors. The following machine learning algorithms: support vector machine (SVM), artificial neural network (ANN), k-nearest neighbors (KNN), decision tree (DT) and DT-Adaboost were able to yield an accuracy above 90%. The research indicates that the difference in N1 signals can be used as a viable parameter when diagnosing schizophrenia.

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