An investigation of how normalisation and local modelling techniques confound machine learning performance in a mental health study
Machine learning (ML) is increasingly deployed on biomedical studies for biomarker development (feature selection) and diagnostic/prognostic technologies (classification). While different ML techniques produce different feature sets and classification performances, less understood is how upstream da...
Saved in:
Main Authors: | Zhang, Xinxin, Lee, Jimmy, Goh, Wilson Wen Bin |
---|---|
Other Authors: | School of Biological Sciences |
Format: | Article |
Language: | English |
Published: |
2022
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/160994 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Normalisers of subgroups of the modular group
by: Lang, M.L.
Published: (2014) -
How doppelgänger effects in biomedical data confound machine learning
by: Wang, Li Rong, et al.
Published: (2022) -
Deranged coagulation profile secondary to cefazolin use: Case report
by: Ngiam, JN, et al.
Published: (2021) -
Dealing with confounders in omics analysis
by: Goh, Wilson Wen Bin, et al.
Published: (2020) -
A new constitutive model for monodispersed suspensions of spheres at high concentrations
by: Phan-Thien, N., et al.
Published: (2014)