Provably Good Early Detection of Diseases using Non-Sparse Covariance-Regularized Linear Discriminant Analysis

To improve the performance of Linear Discriminant Analysis (LDA) for early detection of diseases using Electronic Health Records (EHR) data, we propose ED – a novel framework for EHR based Early Detection of Diseases on top of Covariance-Regularized LDA models. Specifically, ED employs a non-sparse inverse covariance matrix (or namely precision matrix) estimator derived from graphical lasso and incorporates the estimator into LDA classifiers to improve classification accuracy. Theoretical analysis on ED shows that it can bound the expected error rate of LDA classification, under certain assumptions. Finally, we conducted extensive experiments using a large-scale realworld EHR dataset – CHSN. We compared our solution with other regularized LDA and downstream classifiers. The result shows ED outperforms all baselines and backups our theoretical analysis.