Prediction of dementia patients: A comparative approach using parametric vs. non parametric classifiers
In this paper, we report a comparison study of 7 non parametric classifiers (Multilayer perceptron Neural Networks, Radial Basis Function Neural Networks, SupportVectorMachines, CART, CHAID and QUEST Classification trees and Random Forests) as compared to Linear Discriminant Analysis, Quadratic Disc...
| Autor principal: | |
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| Outros Autores: | , , , |
| Formato: | conferenceObject |
| Idioma: | eng |
| Publicado em: |
2012
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| Texto completo: | http://hdl.handle.net/10400.12/1691 |
| País: | Portugal |
| Oai: | oai:repositorio.ispa.pt:10400.12/1691 |
| Resumo: | In this paper, we report a comparison study of 7 non parametric classifiers (Multilayer perceptron Neural Networks, Radial Basis Function Neural Networks, SupportVectorMachines, CART, CHAID and QUEST Classification trees and Random Forests) as compared to Linear Discriminant Analysis, Quadratic Discriminant Analysis and Logistic Regression tested in a real data application of mild cognitive impaired elderly patients conversion to dementia. When classification results are compared both on overall accuracy, specificity and sensitivity, Linear Discriminant Analysis and Random Forests rank first among all the classifiers. |
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