ANFIS system: An algorithm for diagnosing and classifying the levels of depression in the elderly

Introduction: The diagnosis and classification of depression as the most common abnormal psychological disorder in the elderly has received less attention. The aim of the study was to use the ANFIS system to automatically process information in order to provide an appropriate algorithm for predicting the depression of the elderly. Method: The applied study was performed at the Gonbad Kavous Elderly Care Center. A total of 30 elderly people were selected as available samples and the data were collected by clinical interview and GDS scale. MATLABR2016b software was used to implement the equations and functions defined in the ANFIS system layers. Using Pearson's correlation technique, six clinical variables influencing elderly depression were selected as inputs to the ANFIS model. The data were randomly divided into two groups of training and experiments at a ratio of 30:70. System performance appraisal was evaluated using turbulence matrix and ROC curve. Results: The results showed that the ANFIS system algorithm designed in MATLAB software with a TPR of more than 92.56% and with a FPR of 89.68% and an AUC of 0.83 to 1 was highly accurate in diagnosing and classifying elderly depression. Evaluation of the developed model showed that it was able to accurately predict the levels of depression in the elderly compared to the GDS questionnaire and clinical interview. In addition, the model only encountered a non-significant error in distinguishing between low and normal levels of depression, which can be corrected by specialists with the help of clinical symptoms at the time of the interview. Conclusion: the designed system increases the accuracy of the specialist's diagnosis and can be used during the primary care process as a screening tool for early detection of physical or psychological disorders. Eventually, instead of wasting a lot of time and money to diagnose and classify the disorder, it can be used to evaluate the decision of the treatment protocol and make the necessary corrections to improve the organization's performance.