Abstract Machine learning has been the corner stone in analysing and extracting information
from data and often a problem of missing values is encountered. Missing values occur …

Health care systems generate a huge volume of different types of data. Due to the
complexity and challenges inherent in studying medical information, it is not yet possible to …

The use of medical data for machine learning, including unsupervised methods, such as
clustering, is often restricted by privacy regulations, such as the health insurance portability …

Wearable sensors have gathered tremendous interest for a plethora of applications, yet
there is a void of robust and accurate wearable systems for reliable drug monitoring …

Missing data are common in longitudinal observational and randomized controlled trials in
smart health studies. Multiple-imputation based fuzzy clustering is an emerging non …

Web-delivered clinical trials generate big complex data. To help untangle the heterogeneity
of treatment effects, unsupervised learning methods have been widely applied. However …

Disentangling patients' behavioral variations is a critical step for better understanding an
intervention's effects on individual outcomes. Missing data commonly exist in longitudinal …

Big longitudinal data provide more reliable information for decision making and are common
in all kinds of fields. Trajectory pattern recognition is in an urgent need to discover important …

The accuracy of most classification methods is significantly affected by missing values.
Therefore, this study aimed to propose a data imputation method to handle missing values …

Missing data poses a significant challenge in data science, affecting decision-making
processes and outcomes. Understanding what missing data is, how it occurs, and why it is …