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Driving Simulation in the Clinic: Testing Visual Exploratory Behavior in Daily Life Activities in Patients with Visual Field Defects

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Different models for predicting driving performance in people with brain disorders.

Carrie R H Innes¹, Dominic Lee, Chen Chen

¹Department of Medical Physics and Bioengineering, Christchurch Hospital, 8011, New Zealand. carrie.innes@vanderveer.org.nz

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|November 25, 2010

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Advanced kernel methods accurately predict driving ability in brain disorder patients initially. However, cross-validation reveals overfitting, showing traditional models like logistic regression offer comparable real-world predictive accuracy.

Area of Science:

Neuroscience
Rehabilitation Medicine
Machine Learning in Healthcare

Background:

Assessing driving ability in individuals with brain disorders is crucial for safety and independence.
Computerized sensory-motor and cognitive tests (SMCTests™) offer a standardized approach to evaluate driving-related skills.
Various statistical and machine learning models can be employed to predict on-road driving performance.

Purpose of the Study:

To compare the predictive accuracy of six different modeling approaches for on-road driving outcomes in 501 individuals with brain disorders.
To evaluate the robustness of these models using leave-one-out cross-validation for independent data set prediction.

Main Methods:

Performance data from a computerized battery of driving-related sensory-motor and cognitive tests (SMCTests™) were collected.

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Six modeling techniques 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)).

Models were evaluated for their ability to predict on-road driving Pass/Fail outcomes, with and without cross-validation.

Main Results:

Initially, kernel methods (SVM, PK, KP) showed high accuracy (99%, 99%, 80%) in predicting on-road driving success.
Upon cross-validation, kernel method accuracy significantly decreased (SVM 76%, PK 73%, KP 72%), indicating overfitting.
Discriminant analysis (DA) and binary logistic regression (BLR) maintained stable accuracy (74%, 76%) after cross-validation, outperforming kernel methods on independent data.

Conclusions:

While kernel-based models excel at classifying complex data, they may overfit, leading to inflated accuracy on the training set.
For predicting on-road driving outcomes in independent groups of individuals with brain disorders, traditional models like DA and BLR demonstrate comparable or superior robustness.
Careful validation is essential to ensure the generalizability and reliability of predictive models in clinical applications.