1Research Scholar, Department of Computer Science & Engineering, Motilal Nehru National Institute, Allahabad, Uttar Pradesh, India
2Professor, Department of Computer Science & Engineering, Indian Institute of Technology-BHU, Varanasi, Uttar Pradesh, India
3Professor, Department of Computer Science & Engineering, Motilal Nehru National Institute, Allahabad, Uttar Pradesh, India
*Corresponding author Email id: mohitgangwar@gmail.com
Neuropsychiatry is a complex field and its disease diagnosis depends upon the multiple and overlapping symptoms. Data mining method plays a significant role in the analysis of symptoms for the disease diagnosis. In this paper, we apply different data mining techniques for the diagnosis of five neuropsychiatric diseases. The different data mining techniques that we apply in this paper are based on decision tree and artificial neural network concept. Reduced parameter (Sensitivity analysis) parameters based analysis in combination with decision tree and artificial neural network was also performed. Comparative view of accuracy is performed for reduced and non-reduced parameters.
Neuropsychiatric diseases, Data mining, Sensitivity analysis, Decision tree, EEG, FMRI, Electroencephalogram, Disease diagnosis, Intelligent computing, Clementine software
