الفهرس 
AbstractOnly 14 pages are availabe for public view
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Abstract
COVID-19 infection is growing in a rapid rate. Due to unavailability of specific drugs, early detection of COVID-19 patients is essential for disease cure and control. There is a vital need to detect the disease at early stage and instantly quarantine the infected people. Many research have been going on, however, none of them introduces satisfactory results yet. Thanks to data mining and artificial intelligence techniques that allows the accurate detection of COVID-19 patients. This will prevent the sources of infection as well as helping infected patients to prevent the disease progression and also proper treatments for patients can be taken. In this thesis, a novel detection strategies will be proposed by using data mining techniques to provide smart medical diagnosis. The first strategy, which is called Corona Patients Detection Strategy (CPDS) is concentrated in two contributions. The first is a new Hyb¬rid Feature selection Methodology (HFSM), which elects the most informative features from those extracted from chest Computed Tomography (CT) images for COVID-19 patients and NON-COVID-19 peoples. HFSM is a hybrid methodology as it combines evidence from both wrapper and filter feature selection methods. It consists of two stages, namely; Fast selection Stage (FS2) and Accurate selection Stage (AS2). FS2 relies on filter, while AS2 uses Genetic Algorithm (GA) as a wrapper method. As a hybrid methodology, HFSM elects the significant features for the next detection phase. The second contribution is an Enhanced K-Nearest Neighbor (EKNN) classifier, which avoids the trapping problem of the traditional KNN by adding solid heuristics in choosing the neighbors of the tested item. EKNN depends on measuring the degree of both closeness and strength of each neighbor of the tested item, then elects only the qualified neighbors for classification. Accordingly, EKNN can accurately detect infected patients with the minimum time penalty based on those significant features selected by HFSM technique. For the second strategy, which is called Distance Biased Naïve Bayes (DBNB) is composed of two novel contributions. The first is a new feature selection technique called Advanced Particle Swarm Optimization (APSO) which elects the most informative and significant features for diagnosing COVID-19 patients. APSO is a hybrid method based on both filter and wrapper methods to provide accurate and significant features for the next classification phase. The considered features are extracted from laboratory findings for different cases of people, some of whom are COVID-19 infected while some are not. APSO consists of two sequential feature selection stages, namely; Initial selection Stage (IS2) and Final selection Stage (FS2). IS2 uses filter technique to quickly select the most important features for diagnosing COVID-19 patients while removing the redundant and ineffective ones. This behavior minimizes the computational cost in FS2, which is the next stage of APSO. FS2 uses Binary Particle Swarm Optimization (BPSO) as a wrapper method for accurate feature selection. The second contribution is a new classification model, which combines evidence from statistical and distance based classification models. The proposed classification technique avoids the problems of the traditional NB and consists of two modules; Weighted Naïve Bayes Module (WNBM) and Distance Reinforcement Module (DRM).The proposed DBNB tries to accurately detect infected patients with the minimum time penalty based on the most effective features selected by APSO. The last strategy for detecting COVID-19 infected patients will be introduced, which is called Hybrid Diagnose Strategy (HDS). HDS relies on a novel technique for ranking selected features by projecting them into a proposed Patient Space (PS). A Feature Connectivity Graph (FCG) is constructed which indicates both the weight of each feature as well as the binding degree to other features. The rank of a feature is determined based on two factors; the first is the feature weight, while the second is its binding degree to its neighbors in PS. Then, the ranked features are used to derive the classification model that can classify new persons to decide whether they are infected or not. The classification model is a hybrid model that consists of two classifiers; fuzzy inference engine and Deep Neural Network (DNN). Experimental results have shown that the proposed strategies outperform recent COVID-19 diagnose strategies as they introduce the maximum accuracy with the minimum time penalty.