Intelligent Signal Analysis Using Case-Based Reasoning for Decision Support in Stress Management - Computational Intelligence in Healthcare: Advanced Methodologies - page 159

experiences reminds us of previous situations (i.e. cases) or the situation pattern.
CYRUS [27], [28] developed by Janet Colodner, is the first CBR system. She em-
ployed knowledge as cases and used the indexed memory structure. Many of the early
CBR systems such as CASEY [29], and MEDIATOR [30] were implemented based
on CYRUS's work. The early works exploiting CBR in the medical domain were
done by Konton [29], and Braeiss [31], [32] in the late 1980's.
CBR is suitable in the medical domain especially for its cognitively adequate
model; a facility which integrates different types of knowledge with case representa-
tion which can be obtained from patients records [33]. In particular, diagnosis of a
patient in the medical domain depends on experience. Historically, CBR diagnosis
systems have most commonly been used in the medical domain. A clinician/physician
may start their practice with some initial experience (solved cases), then try to utilize
this past experience to solve a new problem while simultaneously increasing their
experiences (i.e. case base). So, this reasoning process is gaining an increasing accep-
tance in the medical field since it has been found suitable for these kinds of decisions
[34], [35], [36], [37], [38]. Some of the recent medical CBR systems with their appli-
cation domain or context are summarized in table 1.

2.3 Fuzzy Logic

Fuzzy set theory has successfully been applied in handling uncertainties in various
application domains [39] including the medical domain. Fuzzy logic was introduced
by Lotfi Zadeh, a professor at the University of California at Berkley in 1965 [40].
The use of fuzzy logic in medical informatics began in the early 1970s. The concept
of fuzzy logic has been formulated from the fact that human reasoning particularly,
common sense reasoning is approximate in nature. So, it is possible to define inexact
medical entities as fuzzy sets. Fuzzy logic is designed to handle partial truth i.e. truth
values between completely true and completely false. For instance, Fuzzy logic al-
lows a person to be classified as both young and old to be true at the same time. It
explains fuzziness that exists in human thinking processes by using fuzzy values in-
stead of using crisp or binary values. It is a superset of classical Boolean logic. In
fuzzy logic, exact reasoning is treated as a special case of approximate reasoning.
Everything in fuzzy logic appears as a matter of some degree i.e. degrees of member-
ship function or degrees of truth. Using height classification as an example (Table 2),
in Boolean logic if we draw a crisp boundary at 180 cm (Fig. 6), we find that Jerry,
who is 179 cm, is small, while Monica is tall because her height is 181 cm. At the
same time, using fuzzy logic all men are “tall”, but their degrees of membership
depend on their height.

Table 2. The classical 'tall men' example using Crisp and Fuzzy values

Name Height, cm Degree of membership

Boolean Fuzzy

John 208 1 1.00

Monica 181 1 0.82

Jerry 179 0 0.78

Roger 167 0 0.15

Sofia 155 0 0.00