Machine learning and deep learning-based approach in smart healthcare: Recent advances, applications, challenges and opportunities

Anichur Rahman; Tanoy Debnath; Dipanjali Kundu; Md. Saikat Islam Khan; Airin Afroj Aishi; Sadia Sazzad; Mohammad Sayduzzaman; Shahab S. Band; Anichur Rahman; Tanoy Debnath; Dipanjali Kundu; Md. Saikat Islam Khan; Airin Afroj Aishi; Sadia Sazzad; Mohammad Sayduzzaman; Shahab S. Band

doi:10.3934/publichealth.2024004

AIMS Public Health

2024, Volume 11, Issue 1: 58-109. doi: 10.3934/publichealth.2024004

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Machine learning and deep learning-based approach in smart healthcare: Recent advances, applications, challenges and opportunities

1.
Department of CSE, National Institute of Textile Engineering and Research (NITER), Constituent Institute of the University of Dhaka, Savar, Dhaka-1350
2.
Department of CSE, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
3.
Department of CSE, Green University of Bangladesh, 220/D, Begum Rokeya Sarani, Dhaka -1207, Bangladesh
4.
Department of Computing and Information System, Daffodil International University, Savar, Dhaka, Bangladesh
5.
Department of Information Management, International Graduate School of Artificial Intelligence, National Yunlin University of Science and Technology, Taiwan

Received: 19 August 2023 Accepted: 18 December 2023 Published: 05 January 2024

In recent years, machine learning (ML) and deep learning (DL) have been the leading approaches to solving various challenges, such as disease predictions, drug discovery, medical image analysis, etc., in intelligent healthcare applications. Further, given the current progress in the fields of ML and DL, there exists the promising potential for both to provide support in the realm of healthcare. This study offered an exhaustive survey on ML and DL for the healthcare system, concentrating on vital state of the art features, integration benefits, applications, prospects and future guidelines. To conduct the research, we found the most prominent journal and conference databases using distinct keywords to discover scholarly consequences. First, we furnished the most current along with cutting-edge progress in ML-DL-based analysis in smart healthcare in a compendious manner. Next, we integrated the advancement of various services for ML and DL, including ML-healthcare, DL-healthcare, and ML-DL-healthcare. We then offered ML and DL-based applications in the healthcare industry. Eventually, we emphasized the research disputes and recommendations for further studies based on our observations.
- machine learning (ML),
- deep learning (DL),
- smart healthcare,
- internet of things (IoT),
- feature extraction,
- data collection,
- data analysis
Citation: Anichur Rahman, Tanoy Debnath, Dipanjali Kundu, Md. Saikat Islam Khan, Airin Afroj Aishi, Sadia Sazzad, Mohammad Sayduzzaman, Shahab S. Band. Machine learning and deep learning-based approach in smart healthcare: Recent advances, applications, challenges and opportunities[J]. AIMS Public Health, 2024, 11(1): 58-109. doi: 10.3934/publichealth.2024004

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Abstract

In recent years, machine learning (ML) and deep learning (DL) have been the leading approaches to solving various challenges, such as disease predictions, drug discovery, medical image analysis, etc., in intelligent healthcare applications. Further, given the current progress in the fields of ML and DL, there exists the promising potential for both to provide support in the realm of healthcare. This study offered an exhaustive survey on ML and DL for the healthcare system, concentrating on vital state of the art features, integration benefits, applications, prospects and future guidelines. To conduct the research, we found the most prominent journal and conference databases using distinct keywords to discover scholarly consequences. First, we furnished the most current along with cutting-edge progress in ML-DL-based analysis in smart healthcare in a compendious manner. Next, we integrated the advancement of various services for ML and DL, including ML-healthcare, DL-healthcare, and ML-DL-healthcare. We then offered ML and DL-based applications in the healthcare industry. Eventually, we emphasized the research disputes and recommendations for further studies based on our observations.

Acknowledgments

This study is not funded by any agency and is being conducted by the authors independently.

Conflict of interest

Shahab S. Band is an editorial board member for AIMS Public Health and was not involved in the editorial review or the decision to publish this article. All authors declare that there are no competing interests.

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