Paper Info
Title
Ensemble-based regression analysis of multimodal medical data for osteopenia diagnosis
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.
Year
DOI
Venue
2013
10.1016/j.eswa.2012.08.031
Expert Syst. Appl.
Keywords
Field
DocType
areal bone mineral density,medical test,feature-wise ensemble,multimodal feature,multimodal data,clinical data,multimodal medical data,feature-wise ensembling approach,osteopenia diagnosis,ensemble-based regression analysis,bone mineral density,clinical practice,regression
Data mining,Regression,Computer science,Regression analysis,Robustness (computer science),Osteopenia,Curse of dimensionality,Bootstrap aggregating,Artificial intelligence,Overfitting,Ensemble learning,Machine learning
Journal
Volume
Issue
ISSN
40
2
0957-4174
Citations 
PageRank 
References 
3
0.39
16
Authors
4
Name
Order
Citations
PageRank
Wei-Liang Tay1513.52
Chee-Kong Chui224538.34
Sim Heng Ong342644.63
Alvin Choong-Meng Ng430.39