New Quadratic Discriminant Analysis Algorithms for Correlated Audiometric Data.

ABSTRACT: Paired organs like eyes, ears, and lungs in humans exhibit similarities, and data from these organs often display remarkable correlations. Accounting for these correlations could enhance classification models used in predicting disease phenotypes. To our knowledge, there is limited, if any, literature addressing this topic, and existing methods do not exploit such correlations. For example, the conventional approach treats each ear as an independent observation when predicting audiometric phenotypes and is agnostic about the correlation of data from the two ears of the same person. This approach may lead to information loss and reduce the model performance. In response to this gap, particularly in the context of audiometric phenotype prediction, this paper proposes new quadratic discrimina

SUBMITTER: Guo F 

PROVIDER: S-EPMC11905980 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature