Automated Prediction of Heart Disease Patients Using Sparse Discriminant Analysis

Abstract

Linear Discriminant Analysis (LDA) is an easy and efficient method for pattern classification, while it is also broadly utilized for the initial discovery of diseases using Electronic Health Records (EHR) data. Nonetheless, the performance of LDA for EHR data classification is recurrently influenced by two major factors: poor evaluation of LDA parameters (e.g., covariance matrix), and “linear inseparability” of the EHR data for classification. In this paper, we propose a novel classifier SDA -Sparse Discriminant Analysis method for heart disease detection. The time complexity will be reduced in this algorithm by optimal scoring analysis of LDA and will be comprehensive to execute sparse discrimination through the combination of Gaussians if limits between classes are nonlinear or if subgroups are available inside every class. On the whole, compared to previous techniques, our proposed technique is more appropriate for the diagnosis of heart disease patients with higher accuracy.