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Bohsas H, Alibrahim H, Swed S, Abouainain Y, Aljabali A, Kazan L, Jabban YKE, Mehmood Q, Sawaf B, Eissa N, Alkasem M, Edrees Y, Cherrez-Ojeda I, Fathey S, Rashid G, Hafez W, AbdElrahim E, Osman H, Emran TB, Khan Pathan R, Khandaker MU. Prevalence and knowledge of polycystic ovary syndrome (PCOS) and health-related practices among women of Syria: a cross-sectional study. J Psychosom Obstet Gynaecol 2024; 45:2318194. [PMID: 38635351 DOI: 10.1080/0167482x.2024.2318194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Accepted: 02/08/2024] [Indexed: 04/20/2024] Open
Abstract
Polycystic Ovarian Syndrome (PCOS) is a prevalent metabolic and hormonal disorder affecting women of reproductive age. Limited data exists on Syrian women's PCOS awareness and health behaviors. This study aimed to gauge PCOS prevalence, knowledge, awareness, and health-related practices among Syrian women. A cross-sectional online survey was conducted from 11 February to 27 October 2022, targeting Syrian women aged 18-45. Collaborators from specific medical universities distributed a questionnaire adapted from a Malaysian paper through social media platforms. Out of 1840 surveyed Syrian women, 64.2% were aged 21-29, and 69.6% held bachelor's degrees. Those with a bachelor's degree exhibited the highest mean knowledge score (12.86), and women previously diagnosed with PCOS had a higher mean knowledge score (13.74) than those without. Approximately 27.4% were confirmed PCOS cases, and 38.9% had possible cases. Women with PCOS were 3.41 times more likely to possess knowledge about the condition. The findings suggest a moderate level of PCOS knowledge and health-related practices among Syrian women, emphasizing the need for increased awareness. Consistent local PCOS screening programs, in collaboration with obstetrics and gynecology professionals, are crucial for improving understanding and clinical symptom recognition of this condition among Syrian women.
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Affiliation(s)
| | | | - Sarya Swed
- Faculty of Medicine, Aleppo University, Aleppo, Syria
| | | | - Ahmed Aljabali
- Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan
| | - Lazaward Kazan
- Faculty of Medicine, Altınbaş University, Istanbul, Turkey
| | | | | | - Bisher Sawaf
- Department of Internal Medicine, Hamad Medical Corporation, Doha, Qatar
| | - Nourhan Eissa
- Faculty of Medicine, Damascus University, Damascus, Syria
| | - Meriam Alkasem
- Faculty of Medicine, Damascus University, Damascus, Syria
| | - Yasmine Edrees
- Faculty of Medicine, Damascus University, Damascus, Syria
| | | | | | - Gowhar Rashid
- Department of Amity Medical School, Amity University, Haryana, India
| | - Wael Hafez
- NMC Royal Hospital, Khalifa City, Abu Dhabi, United Arab Emirates
- Medical Research Division, Department of Internal Medicine, The National Research Centre, Cairo, Egypt
| | - Elrashed AbdElrahim
- Radiological Sciences Department, College of Applied Medical Sciences, Taif University, Taif, Saudi Arabia
| | - Hamid Osman
- Radiological Sciences Department, College of Applied Medical Sciences, Taif University, Taif, Saudi Arabia
| | - Talha Bin Emran
- Department of Pharmacy, BGC Trust University Bangladesh, Chittagong, Bangladesh
| | - Refat Khan Pathan
- Department of Computing and Information Systems, School of Engineering and Technology, Sunway University, Bandar Sunway, Malaysia
| | - Mayeen Uddin Khandaker
- Faculty of Graduate Studies, Daffodil International University, Daffodil Smart City, Dhaka, Bangladesh
- Applied Physics and Radiation Technologies Group, CCDCU, School of Engineering and Technology, Sunway University, Bandar Sunway, Malaysia
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Pathan RK, Biswas M, Yasmin S, Khandaker MU, Salman M, Youssef AAF. Sign language recognition using the fusion of image and hand landmarks through multi-headed convolutional neural network. Sci Rep 2023; 13:16975. [PMID: 37813932 PMCID: PMC10562485 DOI: 10.1038/s41598-023-43852-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Accepted: 09/29/2023] [Indexed: 10/11/2023] Open
Abstract
Sign Language Recognition is a breakthrough for communication among deaf-mute society and has been a critical research topic for years. Although some of the previous studies have successfully recognized sign language, it requires many costly instruments including sensors, devices, and high-end processing power. However, such drawbacks can be easily overcome by employing artificial intelligence-based techniques. Since, in this modern era of advanced mobile technology, using a camera to take video or images is much easier, this study demonstrates a cost-effective technique to detect American Sign Language (ASL) using an image dataset. Here, "Finger Spelling, A" dataset has been used, with 24 letters (except j and z as they contain motion). The main reason for using this dataset is that these images have a complex background with different environments and scene colors. Two layers of image processing have been used: in the first layer, images are processed as a whole for training, and in the second layer, the hand landmarks are extracted. A multi-headed convolutional neural network (CNN) model has been proposed and tested with 30% of the dataset to train these two layers. To avoid the overfitting problem, data augmentation and dynamic learning rate reduction have been used. With the proposed model, 98.981% test accuracy has been achieved. It is expected that this study may help to develop an efficient human-machine communication system for a deaf-mute society.
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Affiliation(s)
- Refat Khan Pathan
- Department of Computing and Information Systems, School of Engineering and Technology, Sunway University, 47500, Bandar Sunway, Selangor, Malaysia
| | - Munmun Biswas
- Department of Computer Science and Engineering, BGC Trust University Bangladesh, Chittagong, 4381, Bangladesh
| | - Suraiya Yasmin
- Department of Computer and Information Science, Graduate School of Engineering, Tokyo University of Agriculture and Technology, Koganei, Tokyo, 184-0012, Japan
| | - Mayeen Uddin Khandaker
- Centre for Applied Physics and Radiation Technologies, School of Engineering and Technology, Sunway University, 47500, Bandar Sunway, Selangor, Malaysia.
- Faculty of Graduate Studies, Daffodil International University, Daffodil Smart City, Birulia, Savar, Dhaka, 1216, Bangladesh.
| | - Mohammad Salman
- College of Engineering and Technology, American University of the Middle East, Egaila, Kuwait
| | - Ahmed A F Youssef
- College of Engineering and Technology, American University of the Middle East, Egaila, Kuwait
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Pathan RK, Uddin MA, Paul AM, Uddin MI, Hamd ZY, Aljuaid H, Khandaker MU. Monkeypox genome mutation analysis using a timeseries model based on long short-term memory. PLoS One 2023; 18:e0290045. [PMID: 37611023 PMCID: PMC10446231 DOI: 10.1371/journal.pone.0290045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 07/31/2023] [Indexed: 08/25/2023] Open
Abstract
Monkeypox is a double-stranded DNA virus with an envelope and is a member of the Poxviridae family's Orthopoxvirus genus. This virus can transmit from human to human through direct contact with respiratory secretions, infected animals and humans, or contaminated objects and causing mutations in the human body. In May 2022, several monkeypox affected cases were found in many countries. Because of its transmitting characteristics, on July 23, 2022, a nationwide public health emergency was proclaimed by WHO due to the monkeypox virus. This study analyzed the gene mutation rate that is collected from the most recent NCBI monkeypox dataset. The collected data is prepared to independently identify the nucleotide and codon mutation. Additionally, depending on the size and availability of the gene dataset, the computed mutation rate is split into three categories: Canada, Germany, and the rest of the world. In this study, the genome mutation rate of the monkeypox virus is predicted using a deep learning-based Long Short-Term Memory (LSTM) model and compared with Gated Recurrent Unit (GRU) model. The LSTM model shows "Root Mean Square Error" (RMSE) values of 0.09 and 0.08 for testing and training, respectively. Using this time series analysis method, the prospective mutation rate of the 50th patient has been predicted. Note that this is a new report on the monkeypox gene mutation. It is found that the nucleotide mutation rates are decreasing, and the balance between bi-directional rates are maintained.
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Affiliation(s)
- Refat Khan Pathan
- Department of Computing and Information Systems, School of Engineering and Technology, Sunway University, Selangor, Malaysia
| | - Mohammad Amaz Uddin
- Department of Computer Science and Engineering, Chittagong University of Engineering & Technology, Chittagong, Bangladesh
| | - Ananda Mohan Paul
- Department of Computer Science and Engineering, BGC Trust University Bangladesh, Chittagong, Bangladesh
| | - Md. Imtiaz Uddin
- Department of Pharmacy, State University of Bangladesh, Dhaka, Bangladesh
| | - Zuhal Y. Hamd
- Department of Radiological Sciences, College of Health and Rehabilitation Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Hanan Aljuaid
- Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University (PNU), Riyadh, Saudi Arabia
| | - Mayeen Uddin Khandaker
- Centre for Applied Physics and Radiation Technologies, School of Engineering and Technology, Sunway University, Selangor, Malaysia
- Department of General Educational Development, Faculty of Science and Information Technology, Daffodil International University, Dhaka, Bangladesh
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Absar N, Das EK, Shoma SN, Khandaker MU, Miraz MH, Faruque MRI, Tamam N, Sulieman A, Pathan RK. The Efficacy of Machine-Learning-Supported Smart System for Heart Disease Prediction. Healthcare (Basel) 2022; 10:healthcare10061137. [PMID: 35742188 PMCID: PMC9222326 DOI: 10.3390/healthcare10061137] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 06/13/2022] [Accepted: 06/14/2022] [Indexed: 11/26/2022] Open
Abstract
The disease may be an explicit status that negatively affects human health. Cardiopathy is one of the common deadly diseases that is attributed to unhealthy human habits compared to alternative diseases. With the help of machine learning (ML) algorithms, heart disease can be noticed in a short time as well as at a low cost. This study adopted four machine learning models, such as random forest (RF), decision tree (DT), AdaBoost (AB), and K-nearest neighbor (KNN), to detect heart disease. A generalized algorithm was constructed to analyze the strength of the relevant factors that contribute to heart disease prediction. The models were evaluated using the datasets Cleveland, Hungary, Switzerland, and Long Beach (CHSLB), and all were collected from Kaggle. Based on the CHSLB dataset, RF, DT, AB, and KNN models predicted an accuracy of 99.03%, 96.10%, 100%, and 100%, respectively. In the case of a single (Cleveland) dataset, only two models, namely RF and KNN, show good accuracy of 93.437% and 97.83%, respectively. Finally, the study used Streamlit, an internet-based cloud hosting platform, to develop a computer-aided smart system for disease prediction. It is expected that the proposed tool together with the ML algorithm will play a key role in diagnosing heart diseases in a very convenient manner. Above all, the study has made a substantial contribution to the computation of strength scores with significant predictors in the prognosis of heart disease.
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Affiliation(s)
- Nurul Absar
- Department of Computer Science and Engineering, BGC Trust University Bangladesh, Chittagong 4381, Bangladesh; (N.A.); (E.K.D.); (S.N.S.)
| | - Emon Kumar Das
- Department of Computer Science and Engineering, BGC Trust University Bangladesh, Chittagong 4381, Bangladesh; (N.A.); (E.K.D.); (S.N.S.)
| | - Shamsun Nahar Shoma
- Department of Computer Science and Engineering, BGC Trust University Bangladesh, Chittagong 4381, Bangladesh; (N.A.); (E.K.D.); (S.N.S.)
| | - Mayeen Uddin Khandaker
- Centre for Applied Physics and Radiation Technologies, School of Engineering and Technology, Sunway University, Petaling Jaya 47500, Selangor, Malaysia
- Department of General Educational Development, Faculty of Science and Information Technology, Daffodil International University, DIU Rd, Dhaka 1341, Bangladesh
- Correspondence: author:
| | - Mahadi Hasan Miraz
- Department of Business Analytics, Sunway University, Petaling Jaya 47500, Selangor, Malaysia;
| | - M. R. I. Faruque
- Space Science Center, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia;
| | - Nissren Tamam
- Department of Physics, College of Science, Princess Nourah Bint Abdulrahman University, Riyadh 11671, Saudi Arabia;
| | - Abdelmoneim Sulieman
- Department of Radiology and Medical Imaging, Prince Sattam Bin Abdulaziz University, Alkharj 11942, Saudi Arabia;
| | - Refat Khan Pathan
- Department of Computing and Information Systems, School of Engineering and Technology, Sunway University, Petaling Jaya 47500, Selangor, Malaysia;
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Pathan RK, Biswas M, Khandaker MU. Time series prediction of COVID-19 by mutation rate analysis using recurrent neural network-based LSTM model. Chaos Solitons Fractals 2020; 138:110018. [PMID: 32565626 PMCID: PMC7293453 DOI: 10.1016/j.chaos.2020.110018] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 06/12/2020] [Indexed: 05/20/2023]
Abstract
SARS-CoV-2, a novel coronavirus mostly known as COVID-19 has created a global pandemic. The world is now immobilized by this infectious RNA virus. As of June 15, already more than 7.9 million people have been infected and 432k people died. This RNA virus has the ability to do the mutation in the human body. Accurate determination of mutation rates is essential to comprehend the evolution of this virus and to determine the risk of emergent infectious disease. This study explores the mutation rate of the whole genomic sequence gathered from the patient's dataset of different countries. The collected dataset is processed to determine the nucleotide mutation and codon mutation separately. Furthermore, based on the size of the dataset, the determined mutation rate is categorized for four different regions: China, Australia, the United States, and the rest of the World. It has been found that a huge amount of Thymine (T) and Adenine (A) are mutated to other nucleotides for all regions, but codons are not frequently mutating like nucleotides. A recurrent neural network-based Long Short Term Memory (LSTM) model has been applied to predict the future mutation rate of this virus. The LSTM model gives Root Mean Square Error (RMSE) of 0.06 in testing and 0.04 in training, which is an optimized value. Using this train and testing process, the nucleotide mutation rate of 400th patient in future time has been predicted. About 0.1% increment in mutation rate is found for mutating of nucleotides from T to C and G, C to G and G to T. While a decrement of 0.1% is seen for mutating of T to A, and A to C. It is found that this model can be used to predict day basis mutation rates if more patient data is available in updated time.
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Affiliation(s)
- Refat Khan Pathan
- Department of Computer Science and Engineering, BGC Trust University Bangladesh, Chittagong-4381, Bangladesh
| | - Munmun Biswas
- Department of Computer Science and Engineering, BGC Trust University Bangladesh, Chittagong-4381, Bangladesh
| | - Mayeen Uddin Khandaker
- Centre for Biomedical Physics, School of Healthcare and Medical Sciences, Sunway University, 47500 Bandar Sunway, Selangor, Malaysia
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