SCHIZOPHRENIA PREDICTION USING MACHINE LEARNING TECHNIQUES

Abstract

Schizophrenia is a mental health problem that affects how a person thinks, feels and behaves. It has symptoms like hearing things that're not there having false beliefs and thinking in a confused way. These symptoms can really affect a persons life. Diagnosing schizophrenia on is not easy because there are no simple tests for it. Doctors usually have to look at how a person behaves and their medical history to make a diagnosis. New developments in imaging and computer technology are giving us new ways to detect schizophrenia. Using a kind of scan called Magnetic Resonance Imaging or MRI along with machine learning is showing promise in finding the differences in the brain that are linked to schizophrenia. Machine learning is a way for computers to learn from amounts of data, which can help doctors make more accurate diagnoses. Researchers are trying approaches, like special algorithms and learning models to tell the difference between people with schizophrenia and those without it. Some methods, like Multiple Kernel Learning are working well. This research is about using machine learning to detect schizophrenia and classify it using MRI of the brain. The goal is to help doctors make decisions and diagnose people correctly.
Schizophrenia detection is getting better with the help of machine learning and MRI scans. Schizophrenia is a problem and doctors need all the help they can get to diagnose it. Machine learning is good at finding patterns in data, which can help doctors understand what is going on in the brain of a person with schizophrenia. This can lead to treatment and care for people, with schizophrenia. Schizophrenia research is important and using machine learning and MRI scans is a part of it.