A greedy-based oversampling approach to improve the prediction of mortality in MERS patients
Document Type
Conference Proceeding
Publication Date
6-13-2016
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.
Identifier
84979220351 (Scopus)
ISBN
[9781467395182]
Publication Title
10th Annual International Systems Conference Syscon 2016 Proceedings
External Full Text Location
https://doi.org/10.1109/SYSCON.2016.7490617
Recommended Citation
Turki, Turki and Wei, Zhi, "A greedy-based oversampling approach to improve the prediction of mortality in MERS patients" (2016). Faculty Publications. 10445.
https://digitalcommons.njit.edu/fac_pubs/10445
