Prediction of stone disease by discriminant analysis and artificial neural networks in genetic polymorphisms: a new method
Version of Record online: 16 APR 2003
Volume 91, Issue 7, pages 661–666, May 2003
How to Cite
Chiang, D., Chiang, H.-C., Chen, W.-C. and Tsai, F.-J. (2003), Prediction of stone disease by discriminant analysis and artificial neural networks in genetic polymorphisms: a new method. BJU International, 91: 661–666. doi: 10.1046/j.1464-410X.2003.03067.x
- Issue online: 16 APR 2003
- Version of Record online: 16 APR 2003
- Accepted for publication 10 September 2002
- artificial neural network;
- discriminant analysis;
- single nucleotide polymorphisms;
To use information from genetic polymorphisms and from patients (drinking/exercise habits) to identify their association with stone disease, the main analytical and predictive tools being discriminant analysis (DA) and artificial neural networks (ANNs).
PATIENTS, SUBJECTS AND METHODS
Urinary stone disease is common in Taiwan; the formation of calcium oxalate stone is reportedly associated with genetic polymorphisms but there are many of these. Genotyping requires many individuals and markers because of the complexity of gene-gene and gene-environmental factor interactions. With the development of artificial intelligence, data-mining tools like ANNs can be used to derive more from patient data in predicting disease. Thus we compared 151 patients with calcium oxalate stones and 105 healthy controls for the presence of four genetic polymorphisms; cytochrome p450c17, E-cadherin, urokinase and vascular endothelial growth factor (VEGF). Information about environmental factors, e.g. water, milk and coffee consumption, and outdoor activities, was also collected. Stepwise DA and ANNs were used as classification methods to obtain an effective discriminant model.
With only the genetic variables, DA successfully classified 64% of the participants, but when all related factors (gene and environmental factors) were considered simultaneously, stepwise DA was successful in classifying 74%. The results for DA were best when six variables (sex, VEGF, stone number, coffee, milk, outdoor activities), found by iterative selection, were used. The ANN successfully classified 89% of participants and was better than DA when considering all factors in the model. A sensitivity analysis of the input parameters for ANN was conducted after the ANN program was trained; the most important inputs affecting stone disease were genetic (VEGF), while the second and third were water and milk consumption.
While data-mining tools such as DA and ANN both provide accurate results for assessing genetic markers of calcium stone disease, the ANN provides a better prediction than the DA, especially when considering all (genetic and environmental) related factors simultaneously. This model provides a new way to study stone disease in combination with genetic polymorphisms and environmental factors.