Towards Precision Psychiatry: An Advanced Machine Learning EEG Model for High‐Accuracy Schizophrenia Diagnosis

Schizophrenia lacks clear biological diagnostic markers, but electroencephalography (EEG) has long been studied for distinguishing neural patterns of the disorder. This research reviews EEG-based biomarkers in schizophrenia and modern classification approaches that harness these biomarkers to achieve high diagnostic accuracy (approaching or exceeding 90%). We examine characteristic EEG signal abnormalities—including alterations in frequency band power (e.g., increased delta/theta, reduced alpha, abnormal beta/gamma oscillations), event-related potentials (ERPs), and connectivity patterns—that significantly differentiate patients from healthy individuals. Statistical and machine learning techniques (including support vector machines, random forests, and deep learning models) are discussed for their ability to recognize these patterns. Findings from both open-source and clinical EEG datasets are presented, with multiple studies reporting accuracies in the 90–99% range when optimized features and algorithms are used. Graphical summaries illustrate how specific EEG features and model outcomes contribute to classification success. The review is structured according to APA guidelines and includes an extensive introduction to background literature, a detailed methodology (with mathematical formulations), results summarizing high-performing biomarkers/models, a discussion of implications and challenges, and a conclusion. Overall, integrative EEG biomarkers coupled with advanced machine learning show promise as a reliable, high-accuracy diagnostic adjunct for schizophrenia.