Dementia is one of the most common neurological disorders among the elderly. Identifying those who are of high risk suffering dementia is important to the administration of early treatment in order to slow down the progression of dementia symptoms. However, to achieve accurate classification, significant amount of subject feature information are involved. Hence identification of demented subjects can be transformed into a pattern recognition problem with high-dimensional nonlinear datasets. In this paper, we introduce trace ratio linear discriminant analysis (TR-LDA) for dementia diagnosis. An improved ITR algorithm (iITR) is developed to solve the TR-LDA problem. This novel method can be integrated with advanced missing value imputation method and utilized for the analysis of the nonlinear datasets in many real-world medical diagnosis problems. Finally, extensive simulations are conducted to show the effectiveness of the proposed method. The results demonstrate that our method can achieve higher accuracies for identifying the demented patients than other state-of-art algorithms. Copyright © 2013 IEEE Signal Processing Society.
|Journal||IEEE Signal Processing Letters|
|Publication status||Published - May 2013|
CitationZhao, M., Chan, R. H. M., Tang, P., Chow, T. W. S., & Wong, S. W. H. (2013). Trace ratio linear discriminant analysis for medical diagnosis: A case study of dementia. IEEE Signal Processing Letters, 20(5), 431-434.
- Medical diagnosis
- Dimensionality reduction
- Feature extraction