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: Compared to the standard KNN, CKNNRLD demonstrated improved prediction accuracy, shorter execution time, and reduced computational burden. According to the simulation findings, using the CKNNRLD method for this purpose took less time compared to using the KNN implementation (for N > 100). It predicted the longitudinal responses more accurately and precisely than the equivalent algorithm. For instance, CKNNRLD execution time was approximately 3.7 times faster than the typical KNN execution time in the scenario with N = 2000, T = 5, D = 2, C = 4, E = 1, and R = 1. Since the KNN method needs all of the training data to identify the nearest neighbors, it tends to operate slowly as the number of individuals in longitudinal research increases (for N > 500).

Conclusion: The CKNNRLD algorithm significantly improves accuracy and computational efficiency for predicting longitudinal responses compared to traditional KNN methods. These findings highlight its potential as a valuable tool for researchers with large longitudinal datasets.