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Predicting Outlier Memberships (2000 Patients)

  • Ton J. Cleophas
  • Aeilko H. Zwinderman
Chapter
  • 65 Downloads

Abstract

With large data files, outlier recognition requires a more sophisticated approach than the traditional data plots and regression lines. This chapter is to examine whether BIRCH (balanced iterative reducing and clustering using hierarchies) clustering is able to predict outliers in future patients from a known population.

Supplementary material

333106_2_En_6_MOESM1_ESM.zip (4 kb)
exportanomalydetection (XML 39 kb)
333106_2_En_6_MOESM2_ESM.sav (37 kb)
outlierdetection (SAV 37 kb)

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Ton J. Cleophas
    • 1
  • Aeilko H. Zwinderman
    • 2
  1. 1.Department Medicine Albert Schweitzer HospitalDordrechtThe Netherlands
  2. 2.Academic Medical CenterDepartment Biostatistics and EpidemiologyAmsterdamThe Netherlands

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