Name
Title | ||
|---|---|---|
Prediction of anterior scoliotic spinal curve from trunk surface using support vector regression |
Abstract | ||
|---|---|---|
This paper proposes a framework for the training of learning systems for regression when (i) the number of examples is small and contains interdependencies, and (ii) each sample consists of large quantities of discrete data that are functional in nature. The objective is to achieve robust yet nonlinear relations between inputs and outputs. In this study, laser scans of the trunk surface and reconstructions of spinal data from X-rays from scoliosis patients were functionally represented as surfaces and curves. Leading functional principal component coefficients thereof constituted comprehensive features, and achieved sufficient dimensionality reduction for the prediction of spine from trunk. As a learning method, support vector regression (SVR) was chosen for its strong generalizability capability that stems from penalizing model complexity. A first robust prediction in this research application was obtained, with coefficients of determination for leading outputs of 0.70 and 0.82, respectively, in the test set. Those translated to a spinal curve prediction L"2-error of 3.61mm, comparable to measurement error in data. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1016/j.engappai.2005.03.006 | Eng. Appl. of AI |
Keywords | Field | DocType |
spinal data,support vector regression,anterior scoliotic spinal curve,robust prediction,large quantity,functional principal component,measurement error,comprehensive feature,discrete data,trunk surface,spinal curve prediction,functional data analysis,laser scanning,coefficient of determination,principal component analysis,principal components analysis | Functional data analysis,Dimensionality reduction,Regression,Computer science,Support vector machine,Artificial intelligence,Observational error,Machine learning,Trunk,Principal component analysis,Test set | Journal |
Volume | Issue | ISSN |
18 | 8 | Engineering Applications of Artificial Intelligence |
Citations | PageRank | References |
11 | 1.83 | 6 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Charles Bergeron | 1 | 100 | 8.31 |
| Farida Cheriet | 2 | 482 | 61.48 |
| Janet L Ronsky | 3 | 17 | 4.51 |
| Ronald Zernicke | 4 | 11 | 1.83 |
| Hubert Labelle | 5 | 226 | 38.65 |