Big data analytics for preventive medicine

Muhammad Imran RAZZAK, Muhammad IMRAN, Guandong XU

Research output: Contribution to journalArticlespeer-review

106 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)4417-4451
JournalNeural Computing and Applications
Early online dateMar 2019
Publication statusPublished - May 2020


Razzak, M. I., Imran, M., & Xu, G. (2020). Big data analytics for preventive medicine. Neural Computing and Applications, 32, 4417-4451.


  • Disease prevention
  • Data analytics
  • Healthcare
  • Knowledge discovery
  • Prevention methodologies


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