After the outbreak of the novel coronavirus disease (2019) (COVID-19), a lot of people have been affected around the world. Due to the large number of affected patients in the world, the global health care system has been disrupted and nearly all hospitals around the world has faced a shortage of bed spaces. As a consequence, being able of prediction of the number of COVID-19 cases is extremely important for taking appropriate decision for management of the affected patients. An accurate prediction of the number of COVID-19 cases Can be obtained using the historical data of reported cases as well as some other data affecting the virus outbreak. However, most of the literature has used only historical data to provide a method of predicting COVID-19 cases and has neglected other influential factors. This has led to inaccurate estimates of the number of infected cases with COVID-19. Thus, the present study tries to provide a more accurate estimation of the number of COVID-19 cases by considering both historical data and other effective factors on the virus. For this purpose, data analysis including the development of a network-based neural algorithm [i.e., nonlinear autonomous exogenous input (NARX)] can be adopted. To examine the viability of this algorithm, experiments were conducted using data collected for the number of COVID-19 cases in the five most affected countries on each continent. Our method led to a more accurate prediction than those obtained by the existing methods. Moreover, we performed experiments to extend our method to predict the number of COVID-19 cases in the future during a period between August 2020 and September 2020. Such predictions can be utilized by the government or people in the affected countries to take precautionary measures against the pandemic.
The area of deep learning research (DL) has seen significant growth in recent years and has shown incredible promise for AI in medical applications. In this approach, we have proposed a deep neural network framework for the categorization of feature-based diabetes data. The dataset is classified using the SoftMax layer after features are collected from it using the wrapper approach. Additionally, the network is adjusted through supervised grid research with the training dataset. However, due to the risk of misdiagnosis in a medical context, we evaluated our model using measures such as precision, recall, specificity, and F1 score and obtained superior results. The proposed architecture has been tested using Diabetes 130-US hospital data, which consists of 100,000 patient files with an average of 55 attributes. As well, five machine learning algorithms—Nave Bayes, Random Forests, Decision Trees, Support vector machines, and Ensemble learning—are used to compare results from experiments. The classification accuracy of the ML algorithm was 86%. This research suggests a Deep Learning system based on feature selection for classifying diabetes data. This method has been tested on UCI machine learning data and has outperformed several other classification techniques, reaching 85.61% accuracy in this study.
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