Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.eswa.2012.08.031
Title: Ensemble-based regression analysis of multimodal medical data for osteopenia diagnosis
Authors: Tay, W.-L.
Chui, C.-K. 
Ong, S.-H. 
Ng, A.C.-M.
Keywords: Areal bone mineral density
Diagnostic CT
Ensemble-based systems
Osteoporosis screening
Regression
Issue Date: 1-Feb-2013
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
URI: http://scholarbank.nus.edu.sg/handle/10635/50916
ISSN: 09574174
DOI: 10.1016/j.eswa.2012.08.031
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