Since the diagnosis of antisocial personality disorder is complex, cost- and time-consuming, inaccurate, and prone to human mistakes, a novel machine learning approach for the detection of ASPD based on electroencephalography recordings was developed. Using machine learning and a finer division of electroencephalography bands, the approach achieves satisfactory and reliable results with a balanced accuracy of 77.50 percent. This novel method introduces a new benchmark since there is no such model available for the exact definition and prediction of antisocial behavior. Due to its high level of threat to individuals and society, the developed approach has strong theoretical and practical relevance.
Baumgartl, Hermann; Dikici, Fulya; Sauter, Daniel; and Buettner, Ricardo, "Detecting Antisocial Personality Disorder Using a Novel Machine Learning Algorithm Based on Electroencephalographic Data" (2020). PACIS 2020 Proceedings. 48.
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