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Neurofuzzy models to automate the grading of old-age depression



Manual grading of depression is sometimes difficult due to the subjective signs-symptoms. The aim of this paper is to automate the process of depression grading using a neurofuzzy model (NFM). Two hundred and seventy real-world depression cases are considered in this work. Each case has seven symptoms, which are obtained according to DSM-IV-TR. Each case is graded as ‘mild’ or ‘moderate’. However, in practice, the boundaries of ‘mild’ and ‘moderate’ grading are fuzzy in nature. The paper attempts to solve this fuzzy overlapping zone of these grades. To reduce the number of symptoms, significantly correlated symptoms are mined using a paired t-test. Then, two NFMs have been developed. NFM-1 has been developed with all seven symptoms, while only significantly correlated symptoms have been used to construct the NFM-2 model. Two fuzzy membership functions, such as triangular membership function (TRMF) and Gaussian membership function (GMF) have been considered to note with which better fuzzification could be achieved. The paper concludes that NFM-1 with GMF is the best model with average predicting accuracy of 94.4% and robustness.