Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/2125 2018 2018 Universidad Regional Autonoma de los Andes (UNIANDES Ambato), Ecuador Elizabeth Elizabeth Universidad Regional Autonoma de los Andes (UNIANDES Santo Domingo), Ecuador Roberto Aguilar Berrezueta Universidad Regional Autonoma de los Andes (UNIANDES), Ecuador Neyda Hernandez Bandera Schizophrenia, a complex psychiatric disorder, presents a significant challenge in early diagnosis and intervention. In this study, we introduce an intelligent approach to schizophrenia detection based on the fusion of multivariate electroencephalography (EEG) signals. Our methodology encompasses the integration of EEG data from multiple electrodes into multivariate input segments, which are then passed into a LightGBM (Light Gradient Boosting Machine) classification model. We systematically explore the fusion process, leveraging the spatiotemporal information captured by EEG signals, and employ machine learning to discern subtle patterns indicative of schizophrenia. To evaluate the effectiveness of our approach, we compare our model against state-of-the-art machine learning algorithms.  Our results demonstrate that our LightGBM-based model outperforms existing methods, achieving competitive performance in the accurate identification of individuals with schizophrenia. 2023 2023 42 51 10.54216/FPA.130204 https://www.americaspg.com/articleinfo/3/show/2125