@inproceedings{3901da3c95cc4b7ab02812c9128d8881,
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.",
keywords = "Case-based reasoning, Clustering, Computational cost, Medical diagnosis",
author = "Park, \{Yoon Joo\}",
year = "2014",
doi = "10.3233/978-1-61499-423-7-52",
language = "English",
isbn = "9781614994220",
series = "Studies in Health Technology and Informatics",
publisher = "IOS Press",
pages = "52--55",
booktitle = "Integrating Information Technology and Management for Quality of Care",
note = "12th International Conference on Informatics, Management, and Technology in Healthcare, ICIMTH 2014 ; Conference date: 10-07-2014 Through 13-07-2014",
}