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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 9/2020

16.03.2019 | Cognitive Computing for Intelligent Application and Service

Big data analytics for preventive medicine

verfasst von: Muhammad Imran Razzak, Muhammad Imran, Guandong Xu

Erschienen in: Neural Computing and Applications | Ausgabe 9/2020

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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.
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Metadaten
Titel
Big data analytics for preventive medicine
verfasst von
Muhammad Imran Razzak
Muhammad Imran
Guandong Xu
Publikationsdatum
16.03.2019
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 9/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-019-04095-y

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