Improving Covariance-Regularized Discriminant Analysis for EHR-Based Predictive Analytics of Diseases
Linear Discriminant Analysis (LDA) is a well-known technique for feature extraction and dimension reduction. The performance of classical LDA however, significantly degrades on the High Dimension Low Sample Size (HDLSS) data for the ill-posed inverse problem. Existing approaches for HDLSS data classification typically assume the data in question are with Gaussian distribution and deal the HDLSS classification problem with regularization. However, these assumptions are too strict to hold in many emerging real-life applications, such as enabling personalized predictive analysis using Electronic Health Records (EHRs) data collected from an extremely limited number of patients who have been diagnosed with or without the target disease for prediction. In this paper, we revised the problem of predictive analysis of disease using personal EHR data and LDA classifier. To fill the gap, in this paper, we first studied an analytical model that understands the accuracy of LDA for classifying data with arbitrary distribution. The model gives a theoretical upper bound of LDA error rate that is controlled by two factors: (1) the statistical convergence rate of (inverse) covariance matrix estimators and (2) the divergence of the training/testing datasets to fitted distributions. To this end, we could lower the error rate by balancing the two factors for better classification performance. Hereby, we further proposed a novel LDA classifier De-Sparse that leverages De-sparsified Graphical Lasso to improve the estimation of LDA, which outperforms state-of-the-art LDA approaches developed for HDLSS data. Such advances and effectiveness are further demonstrated by both theoretical analysis and extensive experiments on EHR datasets.
S. Yang et al., "Improving Covariance-Regularized Discriminant Analysis for EHR-Based Predictive Analytics of Diseases," Applied Intelligence, vol. 51, no. 1, pp. 377 - 395, Springer, Jan 2021.
The definitive version is available at https://doi.org/10.1007/s10489-020-01810-4
Keywords and Phrases
De-Sparsified Graphical Lasso; Electronic Health Records; High Dimension Low Sample Size; Linear Discriminant Analysis
International Standard Serial Number (ISSN)
Article - Journal
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01 Jan 2021