A hybrid neuro-fuzzy system-based ranking function and its application to effective medical information retrieval. (2016)

• Record Type: Journal Article
• Authors: Gayathri, P.; Jaisankar, N.
• Is Part Of: International journal of intelligent information and database systems. Volume 9:Number 3/4(2016)
• Page Start: 248
• Page End: 268
• Publication Date: 2016
• Subjects: medical information retrieval -- MIR: medical information systems -- neuro-fuzzy inference systems -- hybrid ranking function -- HRF -- neural networks -- fuzzy logic -- medical documents -- healthcare services; Database management -- Computer programs -- Periodicals; Information retrieval -- Computer programs -- Periodicals; Information storage and retrieval systems -- Computer programs -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligent agents (Computer software) -- Periodicals; 006.33
• Languages: English
• ISSNs: 1751-5858

Abstract

Retrieval of reliable relevant information is the major concern in medical information retrieval. Among all factors that affect the performance of retrieval system, ranking function is the most important factor. The retrieved documents from large document collection are arranged in the decreasing order of their relevance score by ranking function. A neuro-fuzzy system-based hybrid ranking function (HRF) is proposed in this paper. The proposed ranking function considers weight of document and query with respect to keyword as input features and gives relevance score between document and query as output. Experiments are performed on OHSUMED and PMC benchmark medical document corpus by using 15 experimental queries. The experimental results prove that the proposed HRF performs better when compared with fuzzy logic-based ranking function (FRF) and conventional statistical Euclidean distance-based ranking function (ERF) and cosine similarity-based ranking function (CRF) in terms of precision, recall and F-measure.