The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
A Comprehensive Survey on Machine Learning-Based Big Data Analytics for IoT-Enabled Smart Healthcare System
Open accessAbstract
The outbreak of chronic diseases such as COVID-19 has made a renewed call for providing urgent healthcare facilities to the citizens across the globe. The recent pandemic exposes the shortcomings of traditional healthcare system, i.e., hospitals and clinics alone are not capable to cope with this situation. One of the major technology that aids contemporary healthcare solutions is the smart and connected wearables. The advancement in Internet of Things (IoT) has enabled these wearables to collect data on an unprecedented scale. These wearables gather context-oriented information related to our physical, behavioural and psychological health. The big data generated by wearables and other healthcare devices of IoT is a challenging task to manage that can negatively affect the inference process at the decision centres. Applying big data analytics for mining information, extracting knowledge and making predictions/inferences has recently attracted significant attention. Machine learning is another area of research that has successfully been applied to solve various networking problems such as routing, traffic engineering, resource allocation, and security. Recently, we have seen a surge in the application of ML-based techniques for the improvement of various IoT applications. Although, big data analytics and machine learning are extensively researched, there is a lack of study that exclusively focus on the evolution of ML-based techniques for big data analysis in the IoT healthcare sector. In this paper, we have presented a comprehensive review on the application of machine learning techniques for big data analysis in the healthcare sector. Furthermore, strength and weaknesses of existing techniques along with various research challenges are highlighted. Our study will provide an insight for healthcare practitioners and government agencies to keep themselves well-equipped with the latest trends in ML-based big data analytics for smart healthcare.
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Cites
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Cited by
IoT for Smart Cities: Machine Learning Approaches in Smart Healthcare—A Review
Ghazal, T. M., Hasan, M. K., Alshurideh, M. T., Alzoubi, H. M., Ahmad, M., Akbar, S. S., Al Kurdi, B., & Akour, I. A. (2021) · Future Internet · doi:10.3390/fi13080218 · Cited by 674
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Recent Advancements in Emerging Technologies for Healthcare Management Systems: A Survey
Junaid, S. B., Imam, A. A., Balogun, A. O., De Silva, L. C., Surakat, Y. A., Kumar, G., Abdulkarim, M., Shuaibu, A. N., Garba, A., Sahalu, Y., et al. (2022) · Healthcare · doi:10.3390/healthcare10101940 · Cited by 353
Bourechak, A., Zedadra, O., Kouahla, M. N., Guerrieri, A., Seridi, H., & Fortino, G. (2023) · Sensors · doi:10.3390/s23031639 · Cited by 258
A blockchain-based federated learning mechanism for privacy preservation of healthcare IoT data
Moulahi, W., Jdey, I., Moulahi, T., Alawida, M., & Alabdulatif, A. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107630 · Cited by 103
Metadata
- Authors
- Li, W., Chai, Y., Khan, F., Jan, S. R. U., Verma, S., Menon, V. G., Kavita, & Li, X.
- Venue
- Mobile Networks and Applications
- Published
- 2021-01-06
- Volume
- 26
- Issue
- 1
- Pages
- 234-252
- ISSN
- 1383-469X (ISSN-L)
- 1572-8153
- OpenAlex
- W3118261252
- Cited by
- 413
- References
- 9
- FWCI
- 66.65
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Li, W., Chai, Y., Khan, F., Jan, S. R. U., Verma, S., Menon, V. G., Kavita, & Li, X. (2021). A Comprehensive Survey on Machine Learning-Based Big Data Analytics for IoT-Enabled Smart Healthcare System. Mobile Networks and Applications. https://doi.org/10.1007/s11036-020-01700-6
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