الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
his thesis presents intelligent computer models for analyzing COVID19 disease. These models help to classify cases of COVID-19 into
COVID-19 and Non-COVID with high performance. Data mining and
Machine Learning (ML) techniques are strongly recommended in
developing these expert COVID-19 models which assist physicians in
diagnosing and predicting COVID-19 disease in early stages.
In this thesis, performance of several COVID-19 imaging techniques are
discussed such as: Reverse transcription polymerase chain reaction (RTPCR), X-ray and Computed Tomography (CT). CT technique is one of
modern imaging techniques that takes into account physiological
alterations specific to characteristic of COVID-19. For analysis of COVID-19 in CT images, this thesis presents an
intelligence model which identify positive COVID-19 cases. It presented
the pipeline of medicinal imaging and examination methods included
COVID-19 image acquirement, segmentation and diagnosis, using CT
images. This thesis presented two effective models for single machine
learning (SML) and ensemble machine learning (EML) to detect cases of
COVID-19;The first classification model (SML) was applied with several
algorithms, such as Decision Tree (DT), Artificial Neural Networks
(ANN) and Support Vector Machines (SVM). Results showed that
performance of SVM surpassed other classifiers with a 98.58 % accuracy. The second classification model (EML) was applied with several
algorithms, such as Random Forest (RF), Voting and Bagging, to increase
its accuracy up to 99.60 %, especially using Bagging classifier. Finally,
the results of two proposed models outperformed those of other previous
studies. EML, on other hand, performed even better than SML and is
suggested for real-time application.
This thesis also used effective feature selection (FS) algorithm for
COVID-19 detection model, called PSO-FS algorithm. This proposed
algorithm used particle swarm optimization (PSO) technique as FS search
method for classification COVID-19. The proposed PSO-FS algorithm
employs PSO algorithm to find significant and effective features subset
within overall features set. Support Vector Machine (SVM), K-nearest
neighbor (KNN) classifiers were used as evaluators. The accuracy
reached 99.67% for SVM and 94.27 % for KNN respectively.
Experimental results show that the proposed PSO-FS algorithm
outperforms the other two traditional FS search methods, Genetic
Algorithm (GA) and Greedy Stepwise (GS). This model is an intelligent
and comprehensible model for medical experts.