Neural Network Approaches to Grade Adult Depression

  • Authors:

  • Subhagata Chattopadhyay;Preetisha Kaur;Fethi Rabhi;U. Rajendra Acharya

  • Affiliations:

  • Dept. of Computer Science and Engineering, National Institute of Science and Technology, Berhampur, India 761008;Dept. of Biomedical Engineering, Manipal Institute of Technology, Manipal, India;School of Computer Science and Engineering, The University of New South Wales, Sydney, Australia;Department of ECE, Ngee Ann Polytechnic, Singapore, Singapore

  • Venue:
  • Journal of Medical Systems
  • Year:
  • 2012

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Abstract

Depression is a common but worrying psychological disorder that adversely affects one's quality of life. It is more ominous to note that its incidence is increasing. On the other hand, screening and grading of depression is still a manual and time consuming process that might be biased. In addition, grades of depression are often determined in continuous ranges, e.g., `mild to moderate' and `moderate to severe' instead of making them more discrete as `mild', `moderate', and `severe'. Grading as a continuous range is confusing to the doctors and thus affecting the management plan at large. Given this practical issue, the present paper attempts to differentiate depression grades more accurately using two neural net learning approaches--`supervised', i.e., classification with Back propagation neural network (BPNN) and Adaptive Network-based Fuzzy Inference System (ANFIS) classifiers, and `unsupervised', i.e., `clustering' technique with Self-organizing map (SOM), built in MATLAB 7. The reason for using the supervised and unsupervised learning approaches is that, supervised learning depends exclusively on domain knowledge. Supervision may induce biasness and subjectivities related to the decision-making. Finally, the performance of BPNN and ANFIS are compared and discussed. It was observed that ANFIS, being a hybrid system, performed much better compared to the BPNN classifier. On the other hand, SOM-based clustering technique is also found useful in constructing three distinct clusters. It also assists visualizing the multidimensional data clusters into 2-D.