Classification of schizophrenia from feature-model analysis of bilaterally correlated diagnosis, symptoms, and imaging findings pyramid

Schizophrenia (SZ) is a mental illness that impairs a person's mental capacity, emotionaldispositions, and personal and social quality of life. Manual SZ patient screening is timeconsuming,expensive, and prone to human mistakes. As a result, a autonomous, relativelyaccurate, and reasonably economical system for diagnosing schizophrenia patients isrequired. Machine learning methods are capable of learning subtle hidden patterns fromhigh dimensional imaging data and achieve significant correlations for the classificationof Schizophrenia. In this study, the diverse types of symptoms of the affected person areselected which have the weights assigned by cross-correlations and the model classifiesthe probability of schizophrenia in the person based on the highest weighted symptomspresent in the report of the patient using machine learning classifiers. The classificationis made by various classifiers in which the Support Vector Machine (SVM) gives thebest result. In the neuroscience domain, it has been one of the most popular machinelearningtools. SVM with Radial Basis Function kernel helps to distinguish betweenpatients and healthy controls with significant accuracy of 76% without normalization andPrincipal Component Analysis (PCA). The K nearest neighbor’s algorithm also with nonormalization and PCA showed an accuracy of 73% in predicting SZ which is remarkablyclose to the SVM given the small size dataset.