Missing data can significantly impact dataset integrity and suitability, leading to unreliable
statistical results, distortions, and poor decisions. The presence of missing values in data …

Background This study explored the association of cardiovascular disease (CVD) with
cancer mortality risk in individuals with or without a history of cancer, to better understand …

In a realistic scenario, the dataset has missing values encountered during the data
collection. To effectively build the prediction model, the missingness of the attributes that …

Few studies explored the association of different type of physical activity with all-cause
mortality in hypertension (HBP) participants. A retrospective cohort analysis was performed …

Objective The successful implementation and interpretation of machine learning (ML)
models in epidemiological studies can be challenging without an extensive programming …

Ensuring high-quality data is paramount for maximizing the performance of machine
learning models and business intelligence systems. However, challenges in data quality …

Objective To determine the association between health-related quality of life (HRQOL) and
neck pain, kinesiophobia, and modalities of physical activity in individuals with postoperative …

The development of models to estimate the mortality rate of critically ill patients in the
intensive care unit (ICU) is significantly enhanced by technologies based on artificial …

Tennis is one of the world's most played sports, attracting many spectators to participate in
the game. One of the most essential strokes in a tennis match is serve performance. This …

Many real-world datasets suffer from missing data, which can introduce uncertainty into
ensuing analyses. To address missing data, researchers have been developing, analyzing …