A Systematic Review on Application of Data Mining Techniques in Healthcare Analytics and Data-Driven Decisions

Abstract

AbstractThis systematic review focuses on papers dealing with analytical and/or theoretical research for the application of data mining in healthcare analytics. The integration of healthcare analytics has continued to revolutionize the healthcare industry and has helped in dealing with high readmission rates and medical fraud, among other problematic issues in healthcare. The systematic review employed a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) technique to review research articles published on the applications of data mining in health analytics. The searches process focused on healthcare analytics, data mining, artificial intelligence, and machine learning. The search results were filtered based on subject, year of publication, peer review status, and full-text availability with specific reference to open access journals. Of the over 161 reviewed papers, only 16 were considered as focusing on the theoretical perspective of the application of data mining in healthcare analytics. The review reveals a burgeoning literature that touch on a wide variety of aspects of healthcare, both in various aspects of medical decision support and administration. In view of the improvements afforded by data mining over traditional methods, healthcare providers have enormous incentives to integrate data mining techniques into their systems.KeywordsSystematic reviewPRISMAHealthcare analyticsData miningBig dataHealthcareData analysisIndustry 4.0ResearchData miningAlgorithmsFrameworkEMRAssessmentApplicationsCardiovascularPredictionDiabetesMortalityPrognosis