Linear Discriminant Analysis (LDA) is a simple and effective technique for pattern classification, while it is also widely-used for early detection of diseases using Electronic Health Records (EHR) data. However, the performance of LDA for EHR data classification is frequently affected by two main factors: ill-posed estimation of LDA parameters (e.g., covariance matrix), and "linear inseparability" of the EHR data for classification. To handle these two issues, in this paper, we propose a novel classifier FWDA -- Fisher's Wishart Discriminant Analysis, which is developed as a faster and robust nonlinear classifier. Specifically, FWDA first surrogates the distribution of "potential" inverse covariance matrix estimates using a Wishart distribution estimated from the training data. Then, FWDA samples a group of inverse covariance matrices from the Wishart distribution, predicts using LDA classifiers based on the sampled inverse covariance matrices, and "weighted-averages" the prediction results via Bayesian Voting scheme. The weights for voting are optimally updated to adapt each new input data, so as to enable the nonlinear classification.
S. Yang et al., "Early Detection of Disease using Electronic Health Records and Fisher's Wishart Discriminant Analysis," Procedia Computer Science, vol. 140, pp. 393-402, Elsevier B.V., Nov 2018.
The definitive version is available at https://doi.org/10.1016/j.procs.2018.10.299
Complex Adaptive Systems Conference with Theme: Cyber Physical Systems and Deep Learning, CAS 2018 (2018: Nov. 5-7, Chicago, IL)
Engineering Management and Systems Engineering
Intelligent Systems Center
Keywords and Phrases
Early Detection; Electronic Health Records; Fisher's Wishart Discriminant Analysis; Linear Discriminant Analysis
International Standard Serial Number (ISSN)
Article - Conference proceedings
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