Support vector machines and twin support vector machines for classification of schizophrenia data - Rustam, Zuherman; V. Rampisela, Theresia (International Journal of Engineering and Technology, 2019)
Schizophrenia is a disorder characterized by disturbances in thoughts, perceptions, and behaviors, making it a severe, chronic mental ill-ness. Unfortunately, it is difficult to diagnose it due to lack of physical test, apart from its symptoms resembling that of several other mental illnesses. A former study has used the Northwestern University Schizophrenia Data to identify Schizophrenics from non-Schizophrenics using Support Vector Machines (SVM). Contrastingly, this research used a method that has never been used in Schizophrenia-related problems, the Twin SVM approach. The strategy was employed in classifying the data and comparing the results between various studies. In addition, it successfully classified Schizophrenia data in an accurate manner compared to the SVM method previously used. Precisely, the Twin SVM with Gaussian kernel produced the best final accuracy in classifying Schizophrenia data, 91.0%.