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Hida M, Imai R, Nakamura M, Nakao H, Kitagawa K, Wada C, Eto S, Takeda M, Imaoka M. Investigation of factors influencing low physical activity levels in community-dwelling older adults with chronic pain: a cross-sectional study. Sci Rep 2023; 13:14062. [PMID: 37640818 PMCID: PMC10462701 DOI: 10.1038/s41598-023-41319-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 08/24/2023] [Indexed: 08/31/2023] Open
Abstract
Low levels of physical activity in individuals with chronic pain can lead to additional functional impairment and disability. This study aims to investigate the predictors of low physical activity levels in individuals with chronic pain, and to determine the accuracy of the artificial neural network used to analyze these predictors. Community-dwelling older adults with chronic pain (n = 103) were surveyed for their physical activity levels and classified into low, moderate, or high physical activity level groups. Chronic pain-related measurements, physical function assessment, and clinical history, which all influence physical activity, were also taken at the same time. Logistic regression analysis and analysis of multilayer perceptron, an artificial neural network algorithm, were performed. Both analyses revealed that history of falls was a predictor of low levels of physical activity in community-dwelling older adults. Multilayer perceptron analysis was shown to have excellent accuracy. Our results emphasize the importance of fall prevention in improving the physical activity levels of community-dwelling older adults with chronic pain. Future cross-sectional studies should compare multiple analysis methods to show results with improved accuracy.
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Affiliation(s)
- Mitsumasa Hida
- Department of Rehabilitation, Osaka Kawasaki Rehabilitation University, 158 Mizuma, Kaizuka, Osaka, 597-0104, Japan.
| | - Ryota Imai
- Department of Rehabilitation, Osaka Kawasaki Rehabilitation University, 158 Mizuma, Kaizuka, Osaka, 597-0104, Japan
| | - Misa Nakamura
- Department of Rehabilitation, Osaka Kawasaki Rehabilitation University, 158 Mizuma, Kaizuka, Osaka, 597-0104, Japan
| | - Hidetoshi Nakao
- Department of Physical Therapy, Josai International University, 1 Gumyo, Togane, Chiba, 283-8555, Japan
| | - Kodai Kitagawa
- National Institute of Technology, Hachinohe College, 16-1 Uwanotai, Tamonoki, Hachinohe, Aomori, 039-1192, Japan
| | - Chikamune Wada
- Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Hibikino 2-4, Wakamatsu-ku, Kitakyushu, Fukuoka, 808-0135, Japan
| | - Shinji Eto
- Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Hibikino 2-4, Wakamatsu-ku, Kitakyushu, Fukuoka, 808-0135, Japan
| | - Masatoshi Takeda
- Department of Rehabilitation, Osaka Kawasaki Rehabilitation University, 158 Mizuma, Kaizuka, Osaka, 597-0104, Japan
| | - Masakazu Imaoka
- Department of Rehabilitation, Osaka Kawasaki Rehabilitation University, 158 Mizuma, Kaizuka, Osaka, 597-0104, Japan
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Paul SG, Saha A, Biswas AA, Zulfiker MS, Arefin MS, Rahman MM, Reza AW. Combating Covid-19 using machine learning and deep learning: Applications, challenges, and future perspectives. ARRAY 2023; 17:100271. [PMID: 36530931 PMCID: PMC9737520 DOI: 10.1016/j.array.2022.100271] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 12/05/2022] [Accepted: 12/07/2022] [Indexed: 12/14/2022] Open
Abstract
COVID-19, a worldwide pandemic that has affected many people and thousands of individuals have died due to COVID-19, during the last two years. Due to the benefits of Artificial Intelligence (AI) in X-ray image interpretation, sound analysis, diagnosis, patient monitoring, and CT image identification, it has been further researched in the area of medical science during the period of COVID-19. This study has assessed the performance and investigated different machine learning (ML), deep learning (DL), and combinations of various ML, DL, and AI approaches that have been employed in recent studies with diverse data formats to combat the problems that have arisen due to the COVID-19 pandemic. Finally, this study shows the comparison among the stand-alone ML and DL-based research works regarding the COVID-19 issues with the combinations of ML, DL, and AI-based research works. After in-depth analysis and comparison, this study responds to the proposed research questions and presents the future research directions in this context. This review work will guide different research groups to develop viable applications based on ML, DL, and AI models, and will also guide healthcare institutes, researchers, and governments by showing them how these techniques can ease the process of tackling the COVID-19.
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Affiliation(s)
- Showmick Guha Paul
- Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh
| | - Arpa Saha
- Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh
| | - Al Amin Biswas
- Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh,Corresponding author
| | - Md. Sabab Zulfiker
- Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh
| | - Mohammad Shamsul Arefin
- Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh,Department of Computer Science and Engineering, Chittagong University of Engineering and Technology, Chittagong, Bangladesh
| | - Md. Mahfujur Rahman
- Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh
| | - Ahmed Wasif Reza
- Department of Computer Science and Engineering, East West University, Dhaka, Bangladesh
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Almustafa KM. Covid19-Mexican-Patients' Dataset (Covid19MPD) Classification and Prediction Using Feature Importance. CONCURRENCY AND COMPUTATION : PRACTICE & EXPERIENCE 2022; 34:e6675. [PMID: 34899078 PMCID: PMC8646298 DOI: 10.1002/cpe.6675] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 09/15/2021] [Accepted: 09/24/2021] [Indexed: 06/04/2023]
Abstract
Coronavirus disease, Covid19, pandemic has a great effect on human heath worldwide since it was first detected in late 2019. A clear understanding of the structure of the available Covid19 datasets might give the healthcare provider a better understanding of identifying some of the cases at an early stage. In this article, we will be looking into a Covid19 Mexican Patients' Dataset (Covid109MPD), and we will apply number of machine learning algorithms on the dataset to select the best possible classification algorithm for the death and survived cases in Mexico, then we will study the performance of the enhancement of the specified classifiers in term of their features selection in order to be able to predict sever, and or death, cases from the available dataset. Results show that J48 classifier gives the best classification accuracy with 94.41% and RMSE = 0.2028 and ROC = 0.919, compared to other classifiers, and when using feature selection method, J48 classifier can predict a surviving Covid19MPD case within 94.88% accuracy, and by using only 10 out of the total 19 features.
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Affiliation(s)
- Khaled Mohamad Almustafa
- Department of Information Systems, College of Computer and Information SystemsPrince Sultan UniversityRiyadhKingdom of Saudi Arabia
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