title: Improving real-time efficiency of case-based reasoning for medical diagnosis.
abstract: Conventional case-based reasoning (CBR) does not perform efficiently for high volume dataset because of case-retrieval time. Some previous researches overcome this problem by clustering a case-base into several small groups, and retrieve neighbors within a corresponding group to a target case. However, this approach generally produces less accurate predictive performances than the conventional CBR. This paper suggests a new case-based reasoning method called the Clustering-Merging CBR (CM-CBR) which produces similar level of predictive performances than the conventional CBR with spending significantly less computational cost.
authors.affiliation: Department of Business Administration, Seoul National University of Science and Technology.
authors.forename: Yoon-Joo
authors.initials: YJ
authors.lastname: Park
mesh_terms: D016022:Case-Control Studies; D003199:Computer Systems; D020000:Decision Support Systems, Clinical / Q000458:organization & administration*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005103:Expert Systems; D000069550:Machine Learning; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity