NDLI: A New Hybrid Case-Based Reasoning Approach for Medical Diagnosis Systems

Content Provider	Springer Nature Link
Author	Sharaf El Deen, Dina A. Moawad, Ibrahim F. Khalifa, M. E.
Copyright Year	2014
Abstract	Case-Based Reasoning (CBR) has been applied in many different medical applications. Due to the complexities and the diversities of this domain, most medical CBR systems become hybrid. Besides, the case adaptation process in CBR is often a challenging issue as it is traditionally carried out manually by domain experts. In this paper, a new hybrid case-based reasoning approach for medical diagnosis systems is proposed to improve the accuracy of the retrieval-only CBR systems. The approach integrates case-based reasoning and rule-based reasoning, and also applies the adaptation process automatically by exploiting adaptation rules. Both adaptation rules and reasoning rules are generated from the case-base. After solving a new case, the case-base is expanded, and both adaptation and reasoning rules are updated. To evaluate the proposed approach, a prototype was implemented and experimented to diagnose breast cancer and thyroid diseases. The final results show that the proposed approach increases the diagnosing accuracy of the retrieval-only CBR systems, and provides a reliable accuracy comparing to the current breast cancer and thyroid diagnosis systems.
Starting Page	1
Ending Page	11
Page Count	11
File Format	PDF
ISSN	01485598
Journal	Journal of Medical Systems
Volume Number	38
Issue Number	2
e-ISSN	1573689X
Language	English
Publisher	Springer US
Publisher Date	2014-01-28
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Case-based reasoning (CBR) Rule-based reasoning (RBR) Hybrid medical diagnosis system Adaptation rules Breast cancer diagnosis Thyroid disease diagnosis Statistics for Life Sciences, Medicine, Health Sciences Health Informatics
Content Type	Text
Resource Type	Article
Subject	Health Information Management Information Systems Health Informatics

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