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Neural Network Approaches to Grade Adult Depression

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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.

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Acknowledgement

Authors gratefully acknowledge the psychologists and psychiatrist colleagues who had helped in collecting depression data.

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Authors and Affiliations

  1. Dept. of Computer Science and Engineering, National Institute of Science and Technology, Berhampur, 761008, Orissa, India

    Subhagata Chattopadhyay

  2. Dept. of Biomedical Engineering, Manipal Institute of Technology, Manipal, India

    Preetisha Kaur

  3. School of Computer Science and Engineering, The University of New South Wales, Sydney, New South Wales, Australia

    Fethi Rabhi

  4. Department of ECE, Ngee Ann Polytechnic, Singapore, Singapore

    U. Rajendra Acharya

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  1. Subhagata Chattopadhyay
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  2. Preetisha Kaur
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  4. U. Rajendra Acharya
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Correspondence to Subhagata Chattopadhyay.

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Chattopadhyay, S., Kaur, P., Rabhi, F. et al. Neural Network Approaches to Grade Adult Depression. J Med Syst 36, 2803–2815 (2012). https://doi.org/10.1007/s10916-011-9759-1

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