Cbr Case Based Reasoning

Topics: Angina pectoris, Heart sounds, Hypertension Pages: 27 (3557 words) Published: January 4, 2013
AIML Journal, Volume (5), Issue (1), March, 2005

Abdel-Badeeh M. Salem, Mohamed Roushdy and Rania A. HodHod

Computer Science Department,
Faculty of computer & Information Sciences,
Ain Shams University, Abbassia, Cairo, Egypt
problem is indexed, and subsequently, the indexes are
used to retrieve past cases from case memory. These past
cases lead to a set of prior solutions. Subsequently, the
previous solutions are modified to adapt to the new
solution. Then the proposed solution is tried out. If the
solution succeeds, then it is stored as a working solution
in the case memory; if it fails, the working solution must
be repaired and tested again. Therefore, there are four
key issues in the developing of any CBR system, namely:
(a) case representation and identifying key features, (b)
indexing and retrieving similar cases from the case
memory, (c) measuring case similarity to select the best
match, and (d) modifying the existing solution to fit the
new problem.
CBR has already been applied in a number of
different applications in medicine. CBR is appropriate in
medicine for some important reasons; cognitive
adequateness, explicit experience, duality of objective
and subjective knowledge, automatic acquisition of
subjective knowledge, and system integration [3]. Some
real CBR-systems are: CASEY that gives a diagnosis for
the heart disorders [6], GS.52 which is a diagnostic
support system for dysmorphic syndromes, NIMON is a
renal function monitoring system, COSYL that gives a
consultation for a liver transplanted patient [2] and
ICONS that presents a suitable calculated antibiotics
therapy advise for intensive care patients [15].
This paper presents the development of a case-based
expert system prototype for supporting diagnosis of heart
diseases and is organized as follows: the next section
deals with the domain and knowledge acquisition.
Section 3 presents the case representation and features
extraction. Section 4 deals with case indexing and
retrieval strategies. Section 5 presents the experimental
results, and finally, section 6 summarizes the most
important findings.

In this paper, we have used the Case Based Reasoning
methodology to develop a case-based expert system
prototype for supporting diagnosis of heart diseases. 110
cases were collected for 4 heart diseases namely; mitral
stenosis, left-sided heart failure, stable angina pectoris
and essential hypertension. Each case contains 207
attributes concerning both demographic and clinical data.
After removing the duplicated cases, the system has
trained set of 42 cases for Egyptian cardiac patients.
Statistical analysis has been done to determine the
importance values of the case features. Two retrieval
strategies were investigated namely; induction and
nearest-neighbor approaches. The results indicate that the
nearest neighbor is better than the induction strategy,
where the retrieval accuracy were 100% and 53.8%
respectively. Cardiologists have evaluated the overall
system performance where the system was able to give a
correct diagnosis for thirteen new cases.
Keywords: Expert Systems, Case-Based Reasoning,
Medical Informatics.

1. Introduction
Case-Based Reasoning (CBR) is a general artificial
intelligence paradigm for reasoning from experience.
CBR methodology has been investigated in improving
human decision-making and has received much attention
in developing knowledge-based systems in medicine
[16]. A special issue that includes papers on CBR theory
and applications was published [8, 9]. Unlike the
traditional rule-based approach in which expert
knowledge must be represented in “if-then” rules, a casebased approach allows knowledge to be grouped and stored as cases. The development of this approach has
surged as a key tool for developing a new...

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AIML Journal, Volume (5), Issue (1), March, 2005
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