Genetic-neuro-fuzzy system for grading depression

• Main Authors: Kumar Ashish, Anish Dasari, Subhagata Chattopadhyay, Nirmal Baran Hui
• Format: Article
• Language: English
• Published: Emerald Publishing 2018-01-01
• Series: Applied Computing and Informatics
• Subjects: Depression modeling; Neuro-fuzzy system; Genetic algorithm; Automated tuning; Identification of redundant rules
• Online Access: http://www.sciencedirect.com/science/article/pii/S2210832717301242

Main aim of this study is to develop a software prototype tool for grading and diagnosing depression that will help general physicians for first hand applications. Identification of key symptoms responsible for depression is also another important issue considered in this study. It involves collection of data taken from patients through doctors. Due to several reasons, collection of data in Indian scenario is extremely difficult and thus this tool will be very handy and useful for general physicians working at remote locations. Also, it is possible to collect a data pool through this software model. An intelligent Neuro-Fuzzy model is developed for this purpose. Performance of the said model has been optimized through two approaches. In Approach 1, where a back-propagation algorithm has been considered and in Approach 2, Genetic Algorithm has been used. The model is trained with 78 data and validated with 10 data. Approach 2 superseded Approach 1 in terms of diagnostic accuracy. Therefore, it can be said that the soft computing-based diagnostic models could assist the doctors to make informed decisions. Data for training and validation for this purpose has been collected during 2004–2005 from a Government mental hospital in India.