Boosting K-nearest neighbor regression performance for longitudinal data through a novel learning approach.

ABSTRACT:

Background

Longitudinal studies often require flexible methodologies for predicting response trajectories based on time-dependent and time-independent covariates. To address the complexities of longitudinal data, this study proposes a novel extension of K-Nearest Neighbor (KNN) regression, referred to as Clustering-based KNN Regression for Longitudinal Data (CKNNRLD).

Methods

In CKNNRLD, data are first clustered using the KML algorithm (K-means for longitudinal data), and the nearest neighbors are then searched within the relevant cluster rather than across the entire dataset. The theoretical framework of CKNNRLD was developed and evaluated through extensive simulation studies. Ultimately, the method was applied to a real longitudinal spirometry dataset.

Result

Compar

SUBMITTER: Loeloe MS 

PROVIDER: S-EPMC12482645 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature