دانلود رایگان مقاله انگلیسی یک رویکرد فازی عصبی برای تشخیص اختلال افسردگی پس از زایمان - اشپرینگر 2018

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.

5 Discussion

An artificial intelligence (AI) system is a capable tool for fast and accurate diagnosis for mental illness. A significant amount of research has been conducted on mental health with intentions to automate the diagnostic processes using AI [17, 18]. The technique proposed in this paper is the first of its kind used for diagnosing postpartum depression disorder. In this study, an ANFIS model was developed for diagnosing postpartum depression and the result was compared on ANN using the same dataset. The result indicates that the ANFIS performs better than the ANN for predicting postpartum depression disorder. The ANN used a backward propagation learning algorithm while the ANFIS used a hybrid learning algorithm. Using similar neuro fuzzy system Anish et al. [17] formulated a model for diagnosing depression. Our study validates the assertions by Anish et al. [17] in which clinical symptoms were used to develop the ANFIS although the nature of depression in both studies differs. In our model, a bell membership function was used to map linguistic parameters to their labels because it is capable of approaching a non-fuzzy set and has a nonzero value at all points. The type of membership function used in mapping linguistic variables to linguistic labels might affect the performance of the system [19, 20]. Nevertheless, the current study differs from Anish et al. [17] where probability was used to represent the value of each symptom.

5.1 Conclusion

In this paper, we designed an ANFIS structure for diagnosing postpartum depression and it yielded an excellent result compared to the ANN. Using this model, a system interface can be designed which will utilize the ANFIS architecture. This will assist the medical practitioner in diagnosing postpartum depression.

5.2 Future work

Further research should attempt to design a more sophisticated neuro-fuzzy model that can accommodate larger clinical symptom base for diagnosing postpartum depression as it may help in a more accurate diagnosis of postpartum depression.