Several recent reviews summarize common missing value analysis methods. However,
none of them provide a systematic and in-depth summary of the analytical challenges and …

Recently, numerous studies have been conducted on Missing Value Imputation (MVI),
intending the primary solution scheme for the datasets containing one or more missing …

The primary goal of this study was to evaluate the major roles of health-related quality of life
(HRQOL) in a 5-year lung cancer survival prediction model using machine learning …

Abstract The University of California Irvine Heart disease dataset had missing data on
several attributes. The missing data can loss the important information of the attributes, but it …

Objectives The objective of this systematic review was to study the impact of preoperative
physical activity levels on adult cardiac surgical patients' postoperative:(1) major adverse …

Background The main goal of this study is to explore the use of features representing patient-
level electronic health record (EHR) data, generated by the unsupervised deep learning …

OBJECTIVES To develop a model for predicting long-term survival following coronary artery
bypass graft surgery. METHODS This study included 46 573 patients from the Australian and …

Background A complete dataset is essential for biomedical implementation. Due to the
limitation of objective or subjective factors, missing data often occurs, which exerts …

Background and objectives: Physical activity and sedentary behaviors are associated with
frailty. However, it is unknown if different accumulation patterns of these behaviors are linked …

Background Health and medical research funding agencies are increasingly interested in
measuring the impact of funded research. We present a research impact case study for the …