Descriptive, Predictive, and Prescriptive Analytics

  • Christo El Morr
  • Hossam Ali-Hassan
Part of the SpringerBriefs in Health Care Management and Economics book series (BRIEFSHEALTHCARE)


This chapter provides an overview of the descriptive, predictive, and prescriptive analytics landscape. Data mining is first introduced, followed by coverage of the role of machine learning and artificial intelligence in analytics. Supervised and unsupervised learning are compared, along with the different applications that fall under each. The characteristics and role of reports in descriptive analytics are described, along with the extraction of data in a multidimensional environment. Key algorithms, covering different predictive analytics applications, are described in some detail.


Data mining CRISP-DM Machine learning Artificial intelligence Supervised learning Classification Regression Unsupervised learning Clustering Dimension reduction OLAP Multivariate regression Multiple logistic regression Linear discriminant analysis (LDA) Artificial neural networks (ANNs) K-means Principal component analysis (PCA) 


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Copyright information

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Christo El Morr
    • 1
  • Hossam Ali-Hassan
    • 2
  1. 1.School of Health Policy and ManagementYork UniversityTorontoCanada
  2. 2.Department of International StudiesGlendon College, York UniversityTorontoCanada

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