Comparison of Linear Discriminant Analysis with Robust Linear Discriminant Analysis

Abstract

Discriminant analysis is a multivariate method for dividing things into discrete groups and assigning new objects to existing categories. A discriminant function, which is a linear combination of independent variables used to categorize things into two or more groups or categories, is the result of discriminant analysis. The independent variables in a linear discriminant analysis must be multivariate normally distributed, and the covariance matrices for each group must be equal. In linear discriminant analysis, it is also essential to identify outliers because their existence in the data set can undermine the assumptions made by the method and lead to incorrect classification results. Therefore, in discriminant analysis, handling outliers with robust approaches is required. One such robust method in discriminant analysis is the Minimum Covariance Determinant (MCD), which is highly effective in dealing with outliers and relatively easier to apply compared to other robust methods. The aim of this study is to compare the classification results of linear discriminant analysis with robust linear discriminant analysis on the dataset of diabetes patients at RSUD Padangsidimpuan in 2023. The results obtained from this dataset indicate that linear discriminant analysis achieved an accuracy of 85,71%, while robust linear discriminant analysis achieved an accuracy of 80,95%. These findings suggest that the use of liniar discriminant analysis and robustt linear discriminant analysis can yield different results depending on the characteristics of the data and the number of outliers in the dataset.