Please use this identifier to cite or link to this item:
|Title:||Ensemble-based regression analysis of multimodal medical data for osteopenia diagnosis|
|Keywords:||Areal bone mineral density|
|Source:||Tay, W.-L., Chui, C.-K., Ong, S.-H., Ng, A.C.-M. (2013-02-01). Ensemble-based regression analysis of multimodal medical data for osteopenia diagnosis. Expert Systems with Applications 40 (2) : 811-819. ScholarBank@NUS Repository. https://doi.org/10.1016/j.eswa.2012.08.031|
|Abstract:||Areal bone mineral density (aBMD) is used in clinical practice to diagnose osteoporosis. In previous studies, aBMD was estimated from diagnostic computed tomography (dCT) images, but a battery of medical tests was also taken that can be used to improve the regression performance. However, it is difficult to exploit the multimodal data as the additional features have poor informativeness and may lead to overfitting. An ensemble-based framework is proposed to improve the regression accuracy and robustness on multimodal medical data with a high relative dimensionality. Instead of case-wise bootstrap aggregating, a filtering-based metalearner scheme was employed to build feature-wise ensembles. The proposed approach was evaluated on clinical data and was found to be superior to bagging and other ensemble methods. The feature-wise ensembling approach can also be used to automatically determine if any multimodal features are related to bone mineral density. Several blood measurements were identified to be linked with bone mineral density, and a literature search supported the automatic identification results. © 2012 Elsevier Ltd. All rights reserved.|
|Source Title:||Expert Systems with Applications|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Feb 15, 2018
WEB OF SCIENCETM
checked on Jan 30, 2018
checked on Feb 19, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.