Traditional machine learning methods face unique challenges when applied to healthcare
predictive analytics. The high-dimensional nature of healthcare data necessitates labor …

The article focuses on the gentle introduction of Artificial Intelligence and the concepts of
machine learning (ML) and deep learning (DL). The rapid developments made in DL …

In the domain of using DL-based methods in medical and healthcare prediction systems, the
utilization of state-of-the-art deep learning (DL) methodologies assumes paramount …

Brain tumor represents the unnatural growth of cells in the brain and is identified to be one of
the deadliest cancers around the globe. The survival rate of this disease varies with the …

Large-scale population screening and in-home monitoring for patients with Parkinson's
disease (PD) has so far been mainly carried out by traditional healthcare methods and …

Cardiac MRI (CMR) allows non-invasive, non-ionizing assessment of cardiac function and
anatomy in patients with congenital heart disease (CHD). The utility of CMR as a non …

Verbal autopsies (VA) are commonly used in Low-and Medium-Income Countries (LMIC) to
determine cause of death (CoD) where death occurs outside clinical settings, with the most …

Electronic medical records (EMRs) were primarily introduced as a digital health tool in
hospitals to improve patient care, but over the past decade, research works have …

Image cryptography based on chaos algorithms is widely employed in modern security
systems in telemedicine Internet of Things (IoT) applications. One of the main drawbacks of …

A substantial proportion of patients with functional neurological disorders (FND) are being
incorrectly diagnosed with epilepsy because their semiology resembles that of epileptic …