- Title
- Big data analytics for preventive medicine
- Creator
- Razzak, Muhammad; Imran, Muhammad; Xu, Guandong
- Date
- 2020
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/184141
- Identifier
- vital:16451
- Identifier
-
https://doi.org/10.1007/s00521-019-04095-y
- Identifier
- ISBN:0941-0643 (ISSN)
- Abstract
- Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations. © 2019, Springer-Verlag London Ltd., part of Springer Nature.
- Publisher
- Springer
- Relation
- Neural Computing and Applications Vol. 32, no. 9 (2020), p. 4417-4451
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2019, Springer-Verlag London Ltd., part of Springer Nature.
- Rights
- Open Access
- Subject
- 4602 Artificial Intelligence; 4603 Computer Vision and Multimedia Computation; 4611 Machine Learning; Data analytics; Disease prevention; Healthcare; Knowledge discovery; Prevention methodologies
- Full Text
- Reviewed
- Funder
- Deanship of Scientific Research, King Saud University, RG-1435-051
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