The Iraqi healthcare services are struggling to regain their lost momentum. Many physicians and nurses left Iraq because of the current situation in the country. Despite plans of calling back the skilled health workforce, they are still worried by the disadvantages of their return. Hence, technology plays a central role in taking advantage of their profession through the use of telemedicine. Studying the factors that affect the implementation of telemedicine is necessary. Telemedicine covers network services, policy makers, and patient understanding. A framework that includes the influencing factors in adopting telemedicine in Iraq was developed in this study. A questionnaire was distributed among physicians in Baghdad Medical City to examine the hypothesis on each factor. The Statistical Package for the Social Sciences was utilized to verify the reliability of the questionnaire and Cronbach's alpha test shows that the factors have values more than 0.7, which are standard.
The respiratory disease of coronavirus disease 2019 (COVID-19) has wreaked havoc on the economy of every nation by infecting and killing millions of people. This deadly disease has taken a toll on the life of the entire human race, and an exact cure for it is still not developed. Thus, the control and cure of this disease mainly depend on restricting its transmission rate through early detection. The detection of coronavirus infection facilitates the isolation and exclusive care of infected patients. This research paper proposes a novel data mining system that combines the ensemble feature selection method and machine learning classifier for the effective identification of COVID-19 infection. Different feature selection approaches including chi-square test, recursive feature elimination (RFE), genetic algorithm (GA), particle swarm optimization (PSO), and random forest are evaluated for their effectiveness in enhancing the classification accuracy of the machine learning classifiers. The classifiers that are considered in this research work are decision tree, naïve Bayes, K-nearest neighbor (KNN), multilayer perceptron (MLP), and support vector machine (SVM). Two COVID-19 datasets were used for testing from which the best features supporting the dataset were extracted by the proposed system. The performance of the machine learning classifiers based on the ensemble feature selection methods is analyzed.