A greedy-based oversampling approach to improve the prediction of mortality in MERS patients

Turki Turki, Zhi Wei

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Predicting mortality of Middle East respiratory syndrome (MERS) patients with identified outcomes is a core goal for hospitals in deciding whether a new patient should be hospitalized or not in the presence of limited resources of the hospitals. We present an oversampling approach that we call Greedy-Based Oversampling Approach (GBOA). We evaluate our approach and compare it against the standard oversampling approach from a classification perspective on real dataset collected from the Saudi Ministry of Health using two popular supervised classification methods, Random Forests and Support Vector Machines. Our results demonstrate that our approach outperforms the other standard approach from a classification perspective by giving the highest accuracy with statistical significance on the 20 simulations of the real dataset.

Original languageEnglish (US)
Title of host publication10th Annual International Systems Conference, SysCon 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467395182
DOIs
StatePublished - Jun 13 2016
Event10th Annual International Systems Conference, SysCon 2016 - Orlando, United States
Duration: Apr 18 2016Apr 21 2016

Publication series

Name10th Annual International Systems Conference, SysCon 2016 - Proceedings

Other

Other10th Annual International Systems Conference, SysCon 2016
CountryUnited States
CityOrlando
Period4/18/164/21/16

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Control and Systems Engineering

Keywords

  • Classification
  • Intelligent Decision-Making
  • MERS
  • Oversampling
  • Random Forests
  • Support Vector Machines

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