Addressing Missing Data Challenges in Geriatric Health Monitoring: A Study of Statistical and Machine Learning Imputation Methods.

ABSTRACT: In geriatric healthcare, missing data pose significant challenges, especially in systems used for frailty monitoring in elderly individuals. This study explores advanced imputation techniques used to enhance data quality and maintain model performance in a system designed to detect frailty insights. We introduce missing data mechanisms-Missing Completely at Random (MCAR), Missing at Random (MAR), and Missing Not at Random (MNAR)-into a dataset collected from smart bracelets, simulating real-world conditions. Imputation methods, including Expectation-Maximization (EM), matrix completion, Bayesian networks, K-Nearest Neighbors (KNN), Support Vector Machines (SVMs), Generative Adversarial Imputation Networks (GAINs), Variational Autoencoder (VAE), and GRU-D, were evaluated based on normalized

SUBMITTER: Sasu GV 

PROVIDER: S-EPMC11820420 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature