الفهرس 
Chapter 1: INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
There is evidence that early detection of various diseases can improve the
treatment and increase the survival rate of patients. The conventional
method for diagnosing most of the existing diseases depends on human
skills to recognize the occurrence of the convincing pattern. This age-old
diagnosis method may subject to human mistake, imprecise diagnosis,
time-consuming and labor intensive, and causes an unnecessary burden to
radiologists. Moreover, by the right time of the diagnosis completed, it may
already be at a critical stage.
Recently, Computer Aided Diagnosis (CAD) and machine learning systems
have been developed and functional in order to support specialists in
determining the diagnosis decision process. However, medical diagnosis by
most of the existing CAD systems depends on processing different types of
digital images. This thesis presents an efficient CAD system for cancer
diseases diagnosis by gene expression profiles of DNA microarray datasets.
The proposed CAD system combines Intelligent Decision Support System
(IDSS) and Multi-Agent (MA) system.
The IDSS represents the backbone of the entire CAD system. It consists of
two main phases; feature selection/reduction phase, and a classification
phase. In the feature selection and reduction phase, eight diverse methods
are developed. These methods include Genetic Algorithm (GA), Particle
Swarm Optimization (PSO), Correlated-based Feature selection (CFS),
Information Gain (IG), Gain Ratio (GR), Relief-F, Chi-Square, and Support
Vector Machine with Recursive Feature Elimination (SVM-RFE). While, in the
classification phase, three evolutionary machine learning algorithms are
employed, J48, Naïve Bayes (NB), Genetic Algorithm (GA). The IDSS
performs the required cancer diseases classification Abstract
It first receives a gene expression profiles dataset and then performs the
feature selection and classification process. The feature selection is
performed by using one of the eight approaches while the cancer
classification is performed by using one of the three algorithms. Since there
are eight feature selection methods and three classification methods, then
the proposed CAD system allows 24 different IDSSs to be created and used
for cancer diseases classification.
On the other hand, the Multi-Agent (MA) system manages the entire
operation of the CAD system. It first initializes several IDSSs (exactly 24
IDSSs) with the aid of mobile agents and then directs the generated IDSSs
to run concurrently. Finally, a master agent selects the best classification,
as the final report, based on the best classification accuracy returned from
the 24 IDSSs.
The proposed CAD system is implemented in JAVA, and evaluated by using
eight microarray datasets included Breast cancer, Leukemia, Colon tumor,
Central nervous system, Lung cancer Ontario, Lung cancer Michigan,
Diffuse Large B-Cell Lymphoma and Prostate cancer. The main advantage
of the proposed CAD system is that it classifies the cancer diseases
accurately in a very short time. This is because cancer classification is done
in parallel processing manner. Where, the MA system invokes 24 different
IDSS to classify the diseases on the input dataset concurrently before
taking a decision of the best classification result.