Developing new machine learning ensembles for quality spine diagnosis
- 1 January 2015
- journal article
- research article
- Published by Elsevier BV in Knowledge-Based Systems
- Vol. 73, 298-310
- https://doi.org/10.1016/j.knosys.2014.10.012
Abstract
No abstract availableKeywords
This publication has 44 references indexed in Scilit:
- Cost-Effectiveness Analysis of Treatments for Vertebral Compression FracturesApplied Health Economics and Health Policy, 2012
- t-tests, non-parametric tests, and large studies—a paradox of statistical practice?BMC Medical Research Methodology, 2012
- Variations in vertebral body dimensions in women measured by 3D-XA: A longitudinal in vivo studyBone, 2012
- Does vertebral bone marrow fat content correlate with abdominal adipose tissue, lumbar spine bone mineral density, and blood biomarkers in women with type 2 diabetes mellitus?Journal of Magnetic Resonance Imaging, 2011
- Lumbar shape characterization of the neural arch and vertebral body in spondylolysis: A comparative skeletal studyClinical Anatomy, 2011
- Novel use of Onyx for treatment of intracranial vertebral artery dissectionJournal of NeuroInterventional Surgery, 2011
- An incremental ensemble of classifiersArtificial Intelligence Review, 2011
- Vertebral fracture efficacy during risedronate therapy in patients using proton pump inhibitorsOsteoporosis International, 2011
- Combining bagging, boosting, rotation forest and random subspace methodsArtificial Intelligence Review, 2010
- Analysis of the Sagittal Balance of the Spine and Pelvis Using Shape and Orientation ParametersJournal of Spinal Disorders & Techniques, 2005