Early Schizophrenia Detection Using Artificial Intelligence

Abstract

Following years of research, the processes driving schizophrenia (SZ) genesis, recur rence, symptomatically, and therapy remain a mystery. One of the explanations for this condition could be a lack of proper analytic methods to cope with the variety and complexity of SZ. Deep learning algorithms’ (a branch of artificial intelligence (AI) inspired by the nervous system) extraordinary precision in classification and prediction tasks has changed a wide range of scientific domains and is fast penetrating SZ research. Deep learning has the potential to assist doctors in the prediction, dia gnosis, and treatment of SZ. A thorough literature review was conducted to examine existing research on the use of deep learning in the study of psychosis for diagnosis, as well as the use of electroencephalographic data and signal classification. In this study, we propose the suggested methodology; acquisition, pre-processing of the data; extracting the features; dimension reduction; artificial intelligence classifiers. The outcomes are expected to be positive or negative, and different evaluation metrics will be applied. Results from the study are expected to show that the proposed AI classifiers were able to achieve high accuracy in detecting schizophrenia. The findings of this research have important implications for improving early detection and treatment outcomes for individuals with schizophrenia. Future research should focus on further improving the performance of the proposed AI classifiers and exploring their potential for real-world application.