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Classification of schizophrenia from feature-model analysis of bilaterally correlated diagnosis, symptoms, and imaging findings pyramid | ||
Journal of Advanced Medical Sciences and Applied Technologies | ||
دوره 6، شماره 1، اسفند 2021، صفحه 54-63 اصل مقاله (866.32 K) | ||
نوع مقاله: Original Article | ||
شناسه دیجیتال (DOI): 10.30476/jamsat.2021.48385 | ||
نویسندگان | ||
YTanvi Patel1؛ Shreyansh Dalwadi1؛ Nen Bakraniya1؛ Apurva Desai1؛ Nirmal Kachhiya1؛ Het Parikh1؛ Mohammad Javad Gholamzadeh2؛ Ali- Mohammad Kamali2، 3؛ Milad Kazemiha2، 3؛ Prasun Chakrabarti4؛ Mohammad Nami* 2، 3، 5 | ||
1Deptartment of Computer Science, ITM (SLS) Baroda University, Vadodara, India | ||
2DANA Brain Health Institute, Iranian Neuroscience Society-Fars Chapter, Shiraz, Iran | ||
3Department of Neuroscience, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, Iran | ||
4Provost, Techno India JNR, Institute of Technology, Udaipur 313003, Rajasthan, India | ||
5Neuroscience Center, Instituto de Investigaciones Científicas y Servicios de Alta Tecnología (INDICASAT AIP), City of Knowledge, Panama City, Republic of Panama | ||
چکیده | ||
Schizophrenia (SZ) is a mental illness that impairs a person's mental capacity, emotional dispositions, and personal and social quality of life. Manual SZ patient screening is timeconsuming, expensive, and prone to human mistakes. As a result, a autonomous, relatively accurate, and reasonably economical system for diagnosing schizophrenia patients is required. Machine learning methods are capable of learning subtle hidden patterns from high dimensional imaging data and achieve significant correlations for the classification of Schizophrenia. In this study, the diverse types of symptoms of the affected person are selected which have the weights assigned by cross-correlations and the model classifies the probability of schizophrenia in the person based on the highest weighted symptoms present in the report of the patient using machine learning classifiers. The classification is made by various classifiers in which the Support Vector Machine (SVM) gives the best result. In the neuroscience domain, it has been one of the most popular machinelearning tools. SVM with Radial Basis Function kernel helps to distinguish between patients and healthy controls with significant accuracy of 76% without normalization and Principal Component Analysis (PCA). The K nearest neighbor’s algorithm also with no normalization and PCA showed an accuracy of 73% in predicting SZ which is remarkably close to the SVM given the small size dataset. | ||
کلیدواژهها | ||
Schizophrenia (SZ) Classification؛ Healthy Controls (HC)؛ Support Vector Machine (SVM)؛ Magnetic Resonance images (MRI)؛ Principal Component Analysis (PCA)؛ Functional MRI (fMRI)؛ Structural MRI (sMRI)؛ Independent Component Analysis (ICA) | ||
آمار تعداد مشاهده مقاله: 4,682 تعداد دریافت فایل اصل مقاله: 961 |