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A neuro fuzzy approach for the diagnosis of postpartum depression disorder

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Abstract

Postpartum depression is a growing public health problem amongst nursing mothers, which is not given much attention in primary health care settings. It is a type of depression experienced after childbirth that affects an estimated 13–19% of nursing mothers. Postpartum depression is very difficult to diagnose and by concentrating on somatic illnesses, most medical practitioners frequently fail to recognize it. In this paper an Adaptive Neuro Fuzzy Inference System was utilized to predict postpartum depression. Thirty-six data instances were used in training the model. The system had a training error of 7.0706e−005 at epoch 1 and an average testing error of 3.0185. This technique will facilitate the prompt and accurate diagnosis of postpartum depression.

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Authors and Affiliations

  1. Department of Computer Science, University of Benin, Benin City, Edo State, Nigeria

    V. I. Osubor & A. O. Egwali

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  1. V. I. Osubor
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  2. A. O. Egwali
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Correspondence to V. I. Osubor.

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Osubor, V.I., Egwali, A.O. A neuro fuzzy approach for the diagnosis of postpartum depression disorder. Iran J Comput Sci 1, 217–225 (2018). https://doi.org/10.1007/s42044-018-0021-6

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  • DOI: https://doi.org/10.1007/s42044-018-0021-6

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