NDLI: Different models for predicting driving performance in people with brain disorders

Content Provider	IEEE Xplore Digital Library
Author	Innes, C.R.H. Lee, D. Chen Chen Ponder-Sutton, A.M. Jones, R.D.
Copyright Year	2010
Description	Author affiliation: Department of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand (Lee, D.; Chen Chen; Ponder-Sutton, A.M.) \|\| Department of Medical Physics and Bioengineering, Christchurch Hospital, New Zealand (Jones, R.D.) \|\| Department of Medical Physics and Bioengineering, Christchurch Hospital, 8011, New Zealand (Innes, C.R.H.)
Abstract	Data from performance on a computerized battery of driving-related sensory-motor and cognitive tests (SMCTests™) were used to predict outcome on a blinded on-road driving assessment in 501 people with brain disorders. Six modelling approaches were assessed: discriminant analysis (DA), binary logistic regression (BLR), nonlinear causal resource analysis (NCRA), and three kernel methods (product kernel density (PK), kernel-product density (KP), and support vector machine (SVM)). At the classification level, the three kernel methods were more accurate for predicting on-road Pass or Fail (SVM 99%, PK 99%, KP 80%) than the other models (DA 75%, BLR 77%, NCRA 66%). However, accuracy decreased substantially across the kernel models when leave-one-out cross-validation was used to estimate how accurately the models would predict on-road Pass or Fail in an independent referral group (SVM 76%, PK 73%, KP 72%) but remained fairly constant for DA (74%) and BLR (76%). Cross-validation of NCRA was not possible. While kernel-based models are successful at modelling complex data at a classification level, this appears to be due to overfitting of the data which does not improve accuracy in an independent data set over and above the accuracy of other modelling techniques.
Starting Page	5226
Ending Page	5229
File Size	574722
Page Count	4
File Format	PDF
ISBN	9781424441235
ISSN	1557170X
DOI	10.1109/IEMBS.2010.5626280
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-31
Publisher Place	Argentina
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Predictive models Data models Planning Support vector machines Brain models
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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