Healthcare predictive analytics using machine learning and deep learning techniques: a survey
Abstract Healthcare prediction has been a significant factor in saving lives in recent years. In the domain of health care, there is a rapid development of intelligent systems for analyzing complicated data relationships and transforming them into real information for use in the prediction process....
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-08-01
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| Series: | Journal of Electrical Systems and Information Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s43067-023-00108-y |
| _version_ | 1827724092585803776 |
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| author | Mohammed Badawy Nagy Ramadan Hesham Ahmed Hefny |
| author_facet | Mohammed Badawy Nagy Ramadan Hesham Ahmed Hefny |
| author_sort | Mohammed Badawy |
| collection | DOAJ |
| description | Abstract Healthcare prediction has been a significant factor in saving lives in recent years. In the domain of health care, there is a rapid development of intelligent systems for analyzing complicated data relationships and transforming them into real information for use in the prediction process. Consequently, artificial intelligence is rapidly transforming the healthcare industry, and thus comes the role of systems depending on machine learning and deep learning in the creation of steps that diagnose and predict diseases, whether from clinical data or based on images, that provide tremendous clinical support by simulating human perception and can even diagnose diseases that are difficult to detect by human intelligence. Predictive analytics for healthcare a critical imperative in the healthcare industry. It can significantly affect the accuracy of disease prediction, which may lead to saving patients' lives in the case of accurate and timely prediction; on the contrary, in the case of an incorrect prediction, it may endanger patients' lives. Therefore, diseases must be accurately predicted and estimated. Hence, reliable and efficient methods for healthcare predictive analysis are essential. Therefore, this paper aims to present a comprehensive survey of existing machine learning and deep learning approaches utilized in healthcare prediction and identify the inherent obstacles to applying these approaches in the healthcare domain. |
| first_indexed | 2024-03-10T22:09:52Z |
| format | Article |
| id | doaj.art-eeffd8aac48b459fa1c82ad7bbf32726 |
| institution | Directory Open Access Journal |
| issn | 2314-7172 |
| language | English |
| last_indexed | 2024-03-10T22:09:52Z |
| publishDate | 2023-08-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Electrical Systems and Information Technology |
| spelling | doaj.art-eeffd8aac48b459fa1c82ad7bbf327262023-11-19T12:40:31ZengSpringerOpenJournal of Electrical Systems and Information Technology2314-71722023-08-0110114510.1186/s43067-023-00108-yHealthcare predictive analytics using machine learning and deep learning techniques: a surveyMohammed Badawy0Nagy Ramadan1Hesham Ahmed Hefny2Department of Information Systems and Technology, Faculty of Graduate Studies for Statistical Research, Cairo UniversityDepartment of Information Systems and Technology, Faculty of Graduate Studies for Statistical Research, Cairo UniversityDepartment of Computer Sciences, Faculty of Graduate Studies for Statistical Research, Cairo UniversityAbstract Healthcare prediction has been a significant factor in saving lives in recent years. In the domain of health care, there is a rapid development of intelligent systems for analyzing complicated data relationships and transforming them into real information for use in the prediction process. Consequently, artificial intelligence is rapidly transforming the healthcare industry, and thus comes the role of systems depending on machine learning and deep learning in the creation of steps that diagnose and predict diseases, whether from clinical data or based on images, that provide tremendous clinical support by simulating human perception and can even diagnose diseases that are difficult to detect by human intelligence. Predictive analytics for healthcare a critical imperative in the healthcare industry. It can significantly affect the accuracy of disease prediction, which may lead to saving patients' lives in the case of accurate and timely prediction; on the contrary, in the case of an incorrect prediction, it may endanger patients' lives. Therefore, diseases must be accurately predicted and estimated. Hence, reliable and efficient methods for healthcare predictive analysis are essential. Therefore, this paper aims to present a comprehensive survey of existing machine learning and deep learning approaches utilized in healthcare prediction and identify the inherent obstacles to applying these approaches in the healthcare domain.https://doi.org/10.1186/s43067-023-00108-yHealthcare predictionArtificial intelligence (AI)Machine learning (ML)Deep learning (DL)Medical diagnosis |
| spellingShingle | Mohammed Badawy Nagy Ramadan Hesham Ahmed Hefny Healthcare predictive analytics using machine learning and deep learning techniques: a survey Journal of Electrical Systems and Information Technology Healthcare prediction Artificial intelligence (AI) Machine learning (ML) Deep learning (DL) Medical diagnosis |
| title | Healthcare predictive analytics using machine learning and deep learning techniques: a survey |
| title_full | Healthcare predictive analytics using machine learning and deep learning techniques: a survey |
| title_fullStr | Healthcare predictive analytics using machine learning and deep learning techniques: a survey |
| title_full_unstemmed | Healthcare predictive analytics using machine learning and deep learning techniques: a survey |
| title_short | Healthcare predictive analytics using machine learning and deep learning techniques: a survey |
| title_sort | healthcare predictive analytics using machine learning and deep learning techniques a survey |
| topic | Healthcare prediction Artificial intelligence (AI) Machine learning (ML) Deep learning (DL) Medical diagnosis |
| url | https://doi.org/10.1186/s43067-023-00108-y |
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