An automated machine learning model for diagnosing COVID-19 infection

The coronavirus disease 2019 (COVID-19) epidemic still impacts every facet of life and necessitates a fast and accurate diagnosis. The need for an effective, rapid, and precise way to reduce radiologists' workload in diagnosing suspected cases has emerged. This study used the tree-based pipeline optimization tool (TPOT) and many machine learning (ML) algorithms. TPOT is an open-source genetic programming-based AutoML system that optimizes a set of feature preprocessors and ML models to maximize classification accuracy on a supervised classification problem. A series of trials and comparisons with the results of ML and earlier studies discovered that most of the AutoML beat traditional ML in terms of accuracy. A blood test dataset that has 111 variables and 5644 cases were used. In TPOT, 450 pipelines were used, and the best pipeline selected consisted of radial basis function (RBF) Sampler preprocessing and Gradient boosting classifier as the best algorithm with a 99% accuracy rate. © 2023, Institute of Advanced Engineering and Science. All rights reserved.

Classification of Covid-19 Fake News Using Machine Learning Algorithms

Fake news is a fabrication of the original news intentionally to deceive readers. Internet and social media help such news to spread widely and affect individuals and society negatively. Because of the lack of control over writing the posts on social media. The spread of this type of news has become much more than before. We present one of the most societal severe affairs for misinformation, especially in the presidential elections and fake news related to health like COVID-19. Therefore, there is a need for machine learning algorithms to detect and classify all types of fake news that is difficult to be detected by a human and experts. In this paper, Covid-19 FNs are detected using the Term Frequency-Inverse Document Frequency (TF-IDF) as features extraction and two machine learning algorithms (SVM, Multinomial Naive Bayes) as a classifier. The results show that the accuracy of the proposed algorithms is equal to 94.83% and 91.38%, respectively. We conclude that using machine learning algorithms can help detect such fake news based on good achieved accuracy. © 2022 American Institute of Physics Inc.. All rights reserved.

The effects of the antecedent use of inhaled steroid on the clinical course of COVID-19: A retrospective study of asthmatic patients.

BACKGROUND: There is conflicting evidence regarding the effect of asthma and its different therapeutic options on COVID-19 severity and the clinical outcomes. AIM: This study aimed to investigate the relationship between using inhaled corticosteroids (ICS) by asthmatic patients and the severity of COVID-19. MATERIALS AND METHODS: This retrospective observational study was conducted from March 15 to October 23, 2020 and included data of all COVID-19 asthmatic patients (n = 287) at King Abdulaziz Medical City. Twelve patients were excluded due to poor medication history documentation or using ICS for non-asthma indication. Ordinal logistic regression was used to determine the clinical variables that affect COVID-19 severity. The clinical outcomes of ICS and non-ICS users were compared. RESULTS: Of the sample (n = 275), 198 (72%) were using ICS therapy. No significant difference was found between ICS and non-ICS users in disease severity (P = 0.12), mortality (P = 0.45), ICU admission (P = 0.78), and the occurrence of complications. However, the number of days on ventilation were significantly increased in ICS users (P = 0.006). Being prescribed the ICS/LABA combination (adj OR: 0.72 [0.15,1.2]; P = 0.021), being hypertensive (adj OR: 0.98 [0.28,1.6]; P = 0.006), having cancer (adj OR: 1.49 [0.12, 2.8]; P = 0.033), or having diabetes (adj OR: 0.75 [0.09, 1.4]; P = 0.024) could not increase the risk for more severe disease. CONCLUSION: Overall, ICS therapy did not alter the COVID-19 severity or mortality in asthmatic patients. The continued use of ICS during the pandemic should be encouraged to prevent asthma exacerbations.

Using distance learning programs as instructional technology in architectural engineering. Case study- teaching staff of architectural engineering department, alnahrain university

This study aimed primarily to identify the current reality of using instructional technology, especially distance learning programs, in Architectural Engineering Department, Alnahrain University, where the distance learning system was adopted in the course of all study syllabuses at all levels, after it become difficult to go to the universities. In order to complete the classroom requirements - given that Iraq and all countries of the world are included in the curfew due to the outbreak of COVID-19 virus -, and in order to learn about the reality of this experience, which is using the distance learning programs as instructional technology in architecture, the researcher has designed a questionnaire to identify the reality of this experience. After verifying the validity and stability of this questionnaire, the researcher distributed it to respondents from all levels. The researcher used the descriptive and analytical approach, and then he followed many statistical processing such as (T) test, Pearson correlation coefficient and one- way analysis of variance for analyzing the data. The researcher has found out many results, most notably: -The research showed that the distinctive feature of the level professors' views is negative regarding the use of instructional technology in distance learning programs.Finally, the research concluded a number of recommendations and proposals that help to address the research problem, and shed more light on it. © Published under licence by IOP Publishing Ltd.