الفهرس 
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Abstract
The outbreak of Coronavirus (COVID-19) has spread between people around the world at a rapid rate so that the number of infected people and deaths is increasing quickly every day. Therefore, it is a vital process to detect positive cases at an early stage for treatment and controlling the disease from spreading. Several medical tests had been applied for COVID-19 detection in certain injuries, but with limited efficiency. In this thesis, we introduce an intelligent and novel healthcare diagnostic system based on modern technologies like Internet of Things (IoT), fog computing, cloud computing and machine learning. The proposed system would employ an IoT framework to collect data from users to identify suspected coronaviruses cases early, to monitor the patients, and to understand the nature of the virus by collecting and analysing relevant data. A new COVID-19 diagnosis strategy called Feature Correlated Naïve Bayes (FCNB) is introduced and deployed on fog servers. FCNB consists of four phases, which are; Feature selection Phase (FSP), Feature Clustering Phase (FCP), master Feature Weighting Phase (MFWP), and Feature Correlated Naïve Bayes Phase (FCNBP). FSP selects only the most effective features among the extracted features from laboratory tests for both COVID-19 patients and non-COVID-19 people using Genetic Algorithm as a wrapper method. FCP constructs many clusters of features based on the selected features from FSP by using a novel clustering technique. These clusters of features are called master Features (MFs), in which each MF contains a set of dependent features. MFWP assigns a weight value to each MF by using a new weight calculation method. FCNBP is used to classify patients based on weighted Naïve Bayes algorithm with many modifications as the correlation between features. In this thesis, COVID-19 dataset which contains results of Numerical laboratory tests (NLTs) collected from different cases who were admitted to San Raffaele Hospital (Milan, Italy) is used to detect COVID-19 patients. The total number of cases in this dataset is 207. The dataset is divided into 70% for training and 30% for testing .The proposed FCNB strategy has been compared to recent competitive techniques. Experimental results have proven the effectiveness of the FCNB strategy in which it outperforms recent competitive techniques because it achieves (99%) detection accuracy.