Schinzophrenia Detection using Machine Learning

Schizophrenia is a complex mental disorder characterized by disruptions in thinking, perception, and emotional regulation. Early diagnosis is crucial for effective treatment and improved patient outcomes. This abstract explores the application of machine learning in schizophrenia detection. By analyzing diverse data sources, including neuroimaging, genetic, and clinical data, machine learning models can aid in identifying patterns and biomarkers associated with schizophrenia. This approach offers the potential for early and accurate diagnosis, enabling timely interventions and personalized treatment plans. The integration of machine learning into the diagnostic process holds promise for enhancing the understanding and management of schizophrenia, ultimately improving the quality of life for affected individuals.