Document Type : Original Articles
Authors
1
Department of Computer Science and Engineering, Institute of Technology & Management Universe, Dhanora Tank Road, Near Jarod, Vadodara - 391510, Gujarat, India
2
Preclinical Core Facility, Tehran University of Medical Sciences, Tehran, Iran
3
Intelligent quantitative biomedical imaging (iqbmi), Tehran, 1955748171, Iran
4
School of Medical Physics and Medical Engineering, Tehran University of Medical Sciences, Tehran, 14399-55991, Iran
5
Society for Brain Mapping and Therapeutics (SBMT), Brain Mapping Foundation (BMF), Middle East Brain + Initiative, Los Angeles, CA 90272, CA, USA
6
Department of Neuroscience, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, 71348-14336, Iran
7
Students' Research Committee, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
8
Provost, Techno India JNR, Institute of Technology, Udaipur 313003, Rajasthan, India
9
Neuroscience Center, Instituto de Investigaciones Científicas y Servicios de Alta Tecnología (INDICASAT AIP), City of Knowledge, Panama City, Republic of Panama
Abstract
This paper contains an analysis and comparison of different classifiers on different datasets
of Psychiatric Disorders- Personality Disorder, Depression, Anxiety, Schizophrenia
and Alzheimer's disease. Psychiatric disorders are also referred to as mental disorders,
abnormalities of the mind that result in persistent behavior which can seriously cause day
to day function and life. Stochastic in AI refers to if there is any uncertainty or randomness
involved in results and are used during optimization; Using this process also helps to
provide precise results. The study of stochastic process in AI uses mathematical knowledge
and techniques from probability, set theory, calculus, linear algebra and mathematical
analysis like Fourier analysis, real analysis, and functional analysis. this technique is used
to construct neural network for making artificial intelligent mode for processing and
minimizing human effort. This paper contains classifiers like SVM, MLP, LR, KNN, DT,
and RF. Several types of attributes are used and have been trained by Weka tool, MATLAB,
and Python. The results show that the SVM classifier showed the best performance for all
the attributes and disorders researched in this paper.
Keywords
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