Background and objectives Comprehending the research dataset is crucial for obtaining
reliable and valid outcomes. Health analysts must have a deep comprehension of the data …

In today's data-driven world, addressing bias is essential to minimize discriminatory
outcomes and work toward fairness in machine learning models. This paper presents a …

Background Cesarean section-induced postpartum hemorrhage (PPH) potentially causes
anemia and hypovolemic shock in pregnant women. Hence, it is helpful for obstetricians and …

Multi-label classification tasks are relevant in healthcare, as data samples are commonly
associated with multiple interdependent, non-mutually exclusive outcomes. Incomplete label …

In the realm of data analysis, addressing missing data poses a critical challenge with
implications for both research and practical applications. The absence of data points in …

Diabetes mellitus is a condition that impacts the body's usage of blood sugar. If diabetes is
not properly managed or detected in a timely manner, it can be fatal. It may also harm vital …

Predicting post-discharge complications in cardiothoracic surgery is of utmost importance to
improve clinical outcomes. Machine Learning (ML) techniques have been successfully …

Abstract Integrating Artificial Intelligence (AI) in clinical Decision Support Systems (DSS) can
significantly transform healthcare in many ways to improve patient outcomes. Despite these …

Abstract Machine-learning systems are used to improve efficiency and quality of results and
should uphold an impartiality standard above human decisions. Nevertheless, biases are …