… machine learning paradigms for healthcare informatics. … Machine learning paradigms in
healthcare informatics such … of this emerging area using information learning routines that reveal …

… Today, ML is the most growing subfield in computer science and Health Informatics (HI) is
the greatest application challenge [5, 6]. This is not surprising, because in the health domain …

… data handling the data rich filed of healthcare informatics, and the potential role of machine
… healthcare data, the role of machine learning and existing online health informatics tools. …

… Machine learning (ML) is the fastest growing field in computer science, and health informatics
is … Most ML researchers concentrate on automatic machine learning (aML), where great …

… driven models based on machine learning in health informatics. Deep learning, a technique
… in recent years as a powerful tool for machine learning, promising to reshape the future of …

… We conclude by emphasising that the field of health informatics systems based on
machine learning, drawing on disparate datatypes from the ICU, the wider hospital, and from (…

… machine learning can be used for the same. This study discusses the role of the machine
learning paradigm in healthcare … framework for developing machine learning models for type 2 …

… Machine Learning in smart healthcare studies. We have offered a complete evaluation of the
application of ML methods in the healthcare … based on Machine Learning technology to give …

… and ML in health informatics domain has … health informatics namely, (1) Parkinson’s disease,
(2) stress management, (3) postoperative pain, (4) driver monitoring, and (5) remote health …

Abstract Knowledge mining (KM) tends to deliver the tools and associated components to
extract enormous amounts of data for strategic decision-making. Numerous machine learning (…