Abstract Explainable Artificial Intelligence (XAI) is an emerging research topic of machine
learning aimed at unboxing how AI systems' black-box choices are made. This research field …

There is a need of ensuring that learning (ML) models are interpretable. Higher
interpretability of the model means easier comprehension and explanation of future …

Thyroid disease is considered one of the most common health disorders, which may lead to
various health problems. Recent studies reveal that approximately 42 million people in India …

Artificial intelligence (AI) models are increasingly finding applications in the field of
medicine. Concerns have been raised about the explainability of the decisions that are …

Objective To conduct a systematic scoping review of explainable artificial intelligence (XAI)
models that use real-world electronic health record data, categorize these techniques …

Background Artificial intelligence (AI) algorithms have been applied to diagnose
temporomandibular disorders (TMDs). However, studies have used different patient …

Background Machine learning (ML) has been successful in several fields of healthcare,
however the use of ML within bariatric surgery seems to be limited. In this systematic review …

A decision tree (DT) is one of the most popular and efficient techniques in data mining.
Specifically, in the clinical domain, DTs have been widely used thanks to their relatively easy …

Abstract Machine learning (ML) deals with the automated learning of machines without
being programmed explicitly. It focuses on performing data-based predictions and has …

Abstract Machine learning algorithms often contain many hyperparameters whose values
affect the predictive performance of the induced models in intricate ways. Due to the high …