Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 38, Issue 5, Pages 4990-4997Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2010.09.152
Keywords
Multimodel decision support system; Backpropagation neural network; Radial basis function neural network; Support vector machine; Psychiatry problem
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Psychological distress and disabilities are increasingly identified among general population. Psychiatrist availability in rural areas is poor and often general practitioners have to identify and treat psychiatric problems like depression and anxiety. This work proposes a method to identify the psychiatric problems among patients using multimodel decision support system. Backpropagation neural networks (BPNN), radial basis function neural network (RBFNN) and support vector machine (SVM) models are used to design the decision support system. Forty-four factors are considered for feature extraction. The features are collected from 400 patients and divided into four sets of equal size. Three sets of patient features are used to train the decision support system and one set of patient feature are used to evaluate performance of the system. Experimental results show that the proposed method achieves an accuracy of 98.75% for identifying the psychiatric problems. (C) 2010 Elsevier Ltd. All rights reserved.
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