الفهرس 
A Study on Conventional Adaptation Algorithms 40Only 14 pages are availabe for public view
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Abstract
Case based reasoning (CBR) suggests a model of reasoning
that depends on experiences and learning. CBR solves new cases (or examples) by adapting solutions
of retrieved similar cases. However, despite of its importance, adaptation process in CBR is a very
difficult knowledge intensive task, especially for medical diagnosis. This is due to the
complexities of medical domains, which may lead to uncertain diagnosis decisions. Recent studies
show that there is no structured adaptation model that has been yet developed for medical
diagnosis. revealed that reusing diagnostic solutions is a very difficult unsolved problem.
In this thesis two studies on adaptation algorithms have been done. The first is a study on
conventional adaptation algorithms as substitutional and transformational rules. It points out the
appropriateness and the limitations of conventional algorithms for different CBR tasks as planning,
design and diagnosis. from this study, we concluded that the use of conventional adaptation
algorithms for case adaptation is the main bottleneck for CBR particularly in planning, design and
diagnosis tasks. The second is a study on recent adaptation approaches as neural networks and
induction adaptation algorithms. It points out recent directions for overcoming the adaptation
burden. It also points out the main advantages and limitations of each recent approach for CBR
tasks as planning, design and diagnosis. from this study, we concluded that recent adaptation
approaches have shown significant results, when applied in planning and design tasks. However,
case adaptation is still the main bottleneck for diagnosis tasks.
To test the applicability of different adaptation methods in a real medical domain two CBR-based
expert systems prototypes have been developed for thyroid cancer diagnosis; namely CANCER-T and
CANCER-C. Their main goal is to test the applicability of different adaptation methods in a real
medical domain, which is thyroid cancer diagnosis. In CANCER-T, a case- memory of 820 real thyroid
cancer patient cases is built; these cases are obtained from the expert doctors in the
National Cancer
Institute of Egypt. In the retrieval phase, the nearest-neighbor
retrieval algorithm is implemented for case retrieval. While in the adaptation phase, five
adaptation methods have been implemented. These are Reinstantiation, Parameter Adjustment, Local
Search, Transformational and Hanney Inductive Adaptation approach. These methods have shown very
low accuracy rates, which are: 0% for Reinstantiation, Parameter Adjustment and Local Search, this
is because no disease can be substituted for another. 1.578% for Transformational, this is because
an intractable number of adaptation rules is required in order to handle all feature differences,
which is estimated as 244. Also, Hanney Inductive adaptation approach gives an accuracy rate of
only 3.157%, this is because an intractable number of adaptation rules is still required.
In CANCER-C, the same case-memory of 820 cases is used but each case in the case-memory was
decomposed into three sub- cases at three phases of cancer diagnosis; these are the Suspicion, the
To-Be-Sure and the Stage phases for diagnosing thyroid cancer suspicion, type and stage. In the
retrieval phase, the nearest- neighbor algorithm is implemented for case retrieval. While in the
adaptation phase, two recent adaptation methods have been implemented: Hierarchical combined with
Transformational, and Hierarchical combined with Hanney Induction adaptation approach. These
methods have also shown very low accuracy rates as follows: Hierarchical combined with
Transformational gives 6.3
%, 2.89% and 14.4 % at the Suspicion, the To_Be_Sure and the Stage phases receptively. This is
because an intractable number of adaptation rules is still required at each phase; these are
estimated as 218, 215 and 211 at the Suspicion, the To-Be-Sure and the Stage phases respectively.
Also, Hierarchical combined with Hanney Inductive adaptation approach gives 4.2%, 8.7% and 26.25%
at the Suspicion phase, the To-Be-Sure phase and the Stage phase respectively.
Moreover a new hybrid adaptation model for cancer diagnosis has been developed [90, 91]. It
combines transformational and hierarchical adaptation techniques with certainty factors (CF’s) and
artificial neural networks (ANN’s). The model consists of a hierarchy of three phases that
simulates .