A Healthcare Paradigm for Deriving Knowledge Using Online Consumers’ Feedback
Home healthcare agencies (HHCAs) provide clinical care and rehabilitation services to patients in their own homes. The organization’s rules regulate several connected practitioners, doctors, and licensed skilled nurses. Frequently, it monitors a physician or licensed nurse for the facilities and kee...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Healthcare |
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| Online Access: | https://www.mdpi.com/2227-9032/10/8/1592 |
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| author | Aftab Nawaz Yawar Abbas Tahir Ahmad Noha F. Mahmoud Atif Rizwan Nagwan Abdel Samee |
| author_facet | Aftab Nawaz Yawar Abbas Tahir Ahmad Noha F. Mahmoud Atif Rizwan Nagwan Abdel Samee |
| author_sort | Aftab Nawaz |
| collection | DOAJ |
| description | Home healthcare agencies (HHCAs) provide clinical care and rehabilitation services to patients in their own homes. The organization’s rules regulate several connected practitioners, doctors, and licensed skilled nurses. Frequently, it monitors a physician or licensed nurse for the facilities and keeps track of the health histories of all clients. HHCAs’ quality of care is evaluated using Medicare’s star ratings for in-home healthcare agencies. The advent of technology has extensively evolved our living style. Online businesses’ ratings and reviews are the best representatives of organizations’ trust, services, quality, and ethics. Using data mining techniques to analyze HHCAs’ data can help to develop an effective framework for evaluating the finest home healthcare facilities. As a result, we developed an automated predictive framework for obtaining knowledge from patients’ feedback using a combination of statistical and machine learning techniques. HHCAs’ data contain twelve performance characteristics that we are the first to analyze and depict. After adequate pattern recognition, we applied binary and multi-class approaches on similar data with variations in the target class. Four prominent machine learning models were considered: SVM, Decision Tree, Random Forest, and Deep Neural Networks. In the binary class, the Deep Neural Network model presented promising performance with an accuracy of 97.37%. However, in the case of multiple class, the random forest model showed a significant outcome with an accuracy of 91.87%. Additionally, variable significance is derived from investigating each attribute’s importance in predictive model building. The implications of this study can support various stakeholders, including public agencies, quality measurement, healthcare inspectors, and HHCAs, to boost their performance. Thus, the proposed framework is not only useful for putting valuable insights into action, but it can also help with decision-making. |
| first_indexed | 2024-03-09T09:56:22Z |
| format | Article |
| id | doaj.art-6b485fe2e52a4b179f2ef5be7eb29589 |
| institution | Directory Open Access Journal |
| issn | 2227-9032 |
| language | English |
| last_indexed | 2024-03-09T09:56:22Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Healthcare |
| spelling | doaj.art-6b485fe2e52a4b179f2ef5be7eb295892023-12-01T23:45:49ZengMDPI AGHealthcare2227-90322022-08-01108159210.3390/healthcare10081592A Healthcare Paradigm for Deriving Knowledge Using Online Consumers’ FeedbackAftab Nawaz0Yawar Abbas1Tahir Ahmad2Noha F. Mahmoud3Atif Rizwan4Nagwan Abdel Samee5Department of Computer Science, COMSATS University Islamabad, Attock Campus, Attock 43600, PakistanDepartment of Computer Science, COMSATS University Islamabad, Attock Campus, Attock 43600, PakistanDepartment of Computer Science, COMSATS University Islamabad, Attock Campus, Attock 43600, PakistanDepartment of Rehabilitation Sciences, College of Health and Rehabilitation Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Computer Engineering, Jeju National University, Jejusi 63243, KoreaDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaHome healthcare agencies (HHCAs) provide clinical care and rehabilitation services to patients in their own homes. The organization’s rules regulate several connected practitioners, doctors, and licensed skilled nurses. Frequently, it monitors a physician or licensed nurse for the facilities and keeps track of the health histories of all clients. HHCAs’ quality of care is evaluated using Medicare’s star ratings for in-home healthcare agencies. The advent of technology has extensively evolved our living style. Online businesses’ ratings and reviews are the best representatives of organizations’ trust, services, quality, and ethics. Using data mining techniques to analyze HHCAs’ data can help to develop an effective framework for evaluating the finest home healthcare facilities. As a result, we developed an automated predictive framework for obtaining knowledge from patients’ feedback using a combination of statistical and machine learning techniques. HHCAs’ data contain twelve performance characteristics that we are the first to analyze and depict. After adequate pattern recognition, we applied binary and multi-class approaches on similar data with variations in the target class. Four prominent machine learning models were considered: SVM, Decision Tree, Random Forest, and Deep Neural Networks. In the binary class, the Deep Neural Network model presented promising performance with an accuracy of 97.37%. However, in the case of multiple class, the random forest model showed a significant outcome with an accuracy of 91.87%. Additionally, variable significance is derived from investigating each attribute’s importance in predictive model building. The implications of this study can support various stakeholders, including public agencies, quality measurement, healthcare inspectors, and HHCAs, to boost their performance. Thus, the proposed framework is not only useful for putting valuable insights into action, but it can also help with decision-making.https://www.mdpi.com/2227-9032/10/8/1592decision-makinghome healthcarehealthcare paradigmpattern recognitionquality measurementvaluable insights |
| spellingShingle | Aftab Nawaz Yawar Abbas Tahir Ahmad Noha F. Mahmoud Atif Rizwan Nagwan Abdel Samee A Healthcare Paradigm for Deriving Knowledge Using Online Consumers’ Feedback Healthcare decision-making home healthcare healthcare paradigm pattern recognition quality measurement valuable insights |
| title | A Healthcare Paradigm for Deriving Knowledge Using Online Consumers’ Feedback |
| title_full | A Healthcare Paradigm for Deriving Knowledge Using Online Consumers’ Feedback |
| title_fullStr | A Healthcare Paradigm for Deriving Knowledge Using Online Consumers’ Feedback |
| title_full_unstemmed | A Healthcare Paradigm for Deriving Knowledge Using Online Consumers’ Feedback |
| title_short | A Healthcare Paradigm for Deriving Knowledge Using Online Consumers’ Feedback |
| title_sort | healthcare paradigm for deriving knowledge using online consumers feedback |
| topic | decision-making home healthcare healthcare paradigm pattern recognition quality measurement valuable insights |
| url | https://www.mdpi.com/2227-9032/10/8/1592 |
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