Intelligent Decision Support System for Depression Diagnosis Based on Neuro-fuzzy-CBR Hybrid

Abstract

See, stats, and : https : / / www. researchgate. net/ publication/ 267943058 Intelligent Depression -fuzzy- CBR Article DOI : 10 . 5539 / mas . v6n7p79 CITATIONS 15 READS 91 3 , including : Victor . Ekong University 11 SEE Udoinyang University 16 SEE All . Ekong . The . Abstract Depression disorder is common in primary care , but its diagnosis is complex and controversial due to the conflicting , overlapping and confusing nature of the multitude of symptoms , hence the need to retain cases in a case base and reuse effective previous solutions for current cases . This paper proposes a neuro - fuzzy - Case Base Reasoning (CBR) driven decision support system that utilizes solutions to previous cases in assisting physicians in the diagnosis of depression disorder . The system represents depression disorder with 25 symptoms grouped into five categories . Fuzzy logic provided a means for handling imprecise symptoms . Local similarity between the input cases and retrieved cases was achieved using the absolute deviation as the distance metric , while adaptive neuro - fuzzy inference system handled fuzzy rules whose antecedents are the mapped local similarities of each category of symptoms for global similarity measurement , upon which the retrieved cases are ranked . The 5 best matched cases are subjected to the emotional filter of the system for diagnostic decision making . This approach derives strengths from the hybridization since the tools are complementary to one another .