Complex predictive analysis for health care: a comprehensive review

Dolley Srivastava, Himanshu Pandey, Ambuj Kumar Agarwal

Research output: Contribution to journalReview articlepeer-review

13 Scopus citations

Abstract

Healthcare organizations accept information technology in a management system. A huge volume of data is gathered by healthcare system. Analytics offers tools and approaches for mining information from this complicated and huge data. The extracted information is converted into data which assist decision-making in healthcare. The use of big data analytics helps achievement of improved service quality and reduces cost. Both data mining and big data analytics are applied to pharma co-vigilance and methodological perspectives. Using effective load balancing and as little resources as possible, obtained data is accessible to improve analysis. Data prediction analysis is performed throughout the patient data extraction procedure to achieve prospective outcomes. Data aggregation from huge datasets is used for patient information prediction. Most current studies attempt to improve the accuracy of patient risk prediction by using a commercial model facilitated by big data analytics. Privacy concerns, security risks, limited resources, and the difficulty of dealing with massive amounts of data have all slowed the adoption of big data analytics in the healthcare industry. This paper reviews the various effective predictive analytics methods for diverse diseases like heart disease, blood pressure, and diabetes.

Original languageEnglish
Pages (from-to)521-531
Number of pages11
JournalBulletin of Electrical Engineering and Informatics
Volume12
Issue number1
DOIs
StatePublished - 1 Feb 2023
Externally publishedYes

Keywords

  • Data mining
  • Decision making
  • Machine learning
  • Predictive analytics
  • Smart decision support system

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Control and Systems Engineering
  • Information Systems
  • Instrumentation
  • Hardware and Architecture
  • Computer Networks and Communications
  • Control and Optimization
  • Electrical and Electronic Engineering

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