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161 results on '"Rezayi, Sorayya"'

18. Contributors

Author

Aljehane, Nojood O., primary, Bikku, Thulasi, additional, Dass, Pranav, additional, Dhanasekaran, S., additional, Goyal, Sonali, additional, Gupta, Deepak, additional, Gupta, Vedika, additional, Jacob, Thangavel Prem, additional, Jain, Nikita, additional, Jayasankar, T., additional, Jeya, R., additional, Kiran, Siripuri, additional, Kumar, Ambeshwar, additional, Kumar, K. Pradeep Mohan, additional, Kumar, A. Sivanesh, additional, Mansour, Romany F., additional, Maram, Balajee, additional, Murugan, B.S., additional, Neelakandan, S., additional, Paulraj, D., additional, Pawar, Mohandas V., additional, Pawar, Pranav, additional, Pawar, Asha M., additional, Pravin, Albert, additional, Pustokhin, Denis A., additional, Pustokhina, Irina V., additional, R., Ganesh Kumar, additional, Raj, Sushmitha, additional, Rajasekaran, S., additional, Rajasubramanian, R., additional, Ramachandran, Manikandan, additional, Rao, V. Chandra Shekhar, additional, Reddy, A. Pratapa, additional, Rezayi, Sorayya, additional, S., Jebin Bose, additional, Mani Sekhar, S.R., additional, Shankar, K., additional, Siddesh, G.M., additional, Sivabalaselvamani, D., additional, Sowmyalakshmi, R., additional, Toomula, Srilatha, additional, and Venkatesh, P. Maheswara, additional

Published
2022
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24. An evaluation of the quality of COVID-19 websites in terms of HON principles and using DISCERN tool.

Author

Safdari, Reza, Gholamzadeh, Marsa, Saeedi, Soheila, Tanhapour, Mozhgan, and Rezayi, Sorayya

Subjects
CROSS-sectional method, HEALTH, INFORMATION resources, QUALITY assurance, DESCRIPTIVE statistics, STATISTICAL sampling, DATA analysis software, CONTENT analysis, COVID-19 pandemic, WORLD Wide Web
Abstract

Background: As many people relied on information from the Internet for official scientific or academically affiliated information during the COVID-19 pandemic, the quality of information on those websites should be good. Objective: The main purpose of this study was to evaluate a selection of COVID-19-related websites for the quality of health information provided. Method: Using Google and Yahoo, 36 English language websites were selected, in accordance with the inclusion criteria. The two tools were selected for evaluation were the Health on the Net (HON) Code and the 16-item DISCERN tool. Results: Most websites (39%) were related to information for the public, and a small number of them (3%) concerned screening websites in which people could be informed of their possible condition by entering their symptoms. The result of the evaluation by the HON tool showed that most websites were reliable (53%), and 44% of them were very reliable. Based on the assessment results of the Likert-based 16-item DISCERN tool, the maximum and minimum values for the average scores of each website were calculated as 2.44 and 4.25, respectively. Conclusion: Evaluation using two widely accepted tools shows that most websites related to COVID-19 are reliable and useful for physicians, researchers and the public. [ABSTRACT FROM AUTHOR]

Published
2023
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37. Applying Data Mining Approaches for Chronic Kidney Disease Diagnosis

Author

Rezayi, Sorayya, Maghooli, Keivan, and Saeedi, Soheila

Subjects
Data Mining, Chronic Kidney Disease, Supervised Technique, Machine Learning, data mining
Abstract

Kidney disease is one of the most common problems today that many people in the world deal with it. Therefore, in this study, our main objective is to use several computational-based algorithms to classify and diagnose Chronic Kidney Disease. The data applied in our study were publicly available data on Chronic Kidney Disease. Eight classifiers were developed to classify chronic kidney disease into two groups (patient or not). We used the Windows 10 operating system and RapidMiner Studio 9.8 version. The confusion matrix provides us the TP, FP, FN, and TN values; some performance measures were calculated to evaluate the used techniques. Evaluation of data mining algorithms revealed that random forest (with 100 trees), deep learning network (with five hidden layers), and artificial intelligence network (with 0.02 training rate and 100 cycles) reached the highest accuracy rates with 99.09%, 98.06%, and 96.52% respectively. However, it is notable that random forest, support vector machine, and deep learning network achieved 1 for AUC. Data mining on health-related issues can be considered one of the most useful data analysis tools and statistical data. These classification methods are beneficial for specialists in the medical diagnosis process, and by using these techniques, hidden patterns are extracted from the data.

Published
2021

49. Comparing the performance of machine learning techniques for low back pain diagnosis

Author

Bouraghi, Hamid, Rezayi, Sorayya, Saeedi, Soheila, Salimi, Rasoul, Jahani, Meysam, and Abdolmaleki, Sajjad

Abstract

Low back pain is a global health problem that is a major cause of disability in developing and developed countries. Machine learning and data mining algorithms can be used to help diagnose this disease. This study aimed to determine the performance of different machine learning algorithms. Nine machine learning techniques, including support vector machine, decision tree, Naive Bayes, K-nearest neighbours, neural network, random forest, deep learning, auto-MLP, and rule induction, were used to modelling. This study revealed that the highest accuracy was related to the random forest (83.55%) and support vector machine (82.26%) classifiers. As a result, machine learning algorithms have good accuracy in low back pain diagnosis.

Published
2023
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50. Artificial intelligence approaches on X-ray-oriented images process for early detection of COVID-19.

Author

Rezayi, Sorayya, Ghazisaeedi, Marjan, Kalhori, Sharareh, and Saeedi, Soheila

Subjects
*IMAGE processing, *ARTIFICIAL intelligence, *COVID-19, *X-ray imaging, *COVID-19 pandemic, *PYTHON programming language
Abstract

Background: COVID-19 is a global public health problem that is crucially important to be diagnosed in the early stages. This study aimed to investigate the use of artificial intelligence (AI) to process X-ray-oriented images to diagnose COVID-19 disease. Methods: A systematic search was conducted in Medline (through PubMed), Scopus, ISI Web of Science, Cochrane Library, and IEEE Xplore Digital Library to identify relevant studies published until 21 September 2020. Results: We identified 208 papers after duplicate removal and filtered them into 60 citations based on inclusion and exclusion criteria. Direct results sufficiently indicated a noticeable increase in the number of published papers in July-2020. The most widely used datasets were, respectively, GitHub repository, hospital-oriented datasets, and Kaggle repository. The Keras library, Tensorflow, and Python had been also widely employed in articles. X-ray images were applied more in the selected articles. The most considerable value of accuracy, sensitivity, specificity, and Area under the ROC Curve was reported for ResNet18 in reviewed techniques; all the mentioned indicators for this mentioned network were equal to one (100%). Conclusion: This review revealed that the application of AI can accelerate the process of diagnosing COVID-19, and these methods are effective for the identification of COVID-19 cases exploiting Chest X-ray images. [ABSTRACT FROM AUTHOR]

Published
2022
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