Although advances in speech processing have facilitated the prognostic assessment of
patients with Parkinson's Disease (PD), there is no objective method towards its early …

Abstract Typically, supervised Machine Learning (ML)-based image classifiers leverage
algorithms derived from either Artificial Neural Networks (ANNs) or optimal separating …

Surface electromyography (sEMG) is a non-invasive tool that can aid physiological
assessment of knee disorders towards clinical interventions. Machine Learning (ML) is …

Abstract Despite advances in Machine Learning (ML) algorithms, the clinical viability of ML-
based decision support systems (DSS) to predict the prognosis of hepatitis remains limited …

This study investigated the application of a novel hybrid artificial intelligence (AI)-based
classifier for aiding prediction of the prognosis in patients with chronic hepatitis. Nineteen …

Postoperative discharge decision-making is a critical process that determines not only
postoperative patient outcomes and, in some cases, their survival, but also the management …

Recent advances in the state-of-the-art open-source kernel functions for support vector
machines (SVMs) have widened the choices of benchmark kernels for Machine Learning …

An early detection of motor impairments in children is essential to improve self-care.
Nevertheless, it may not be straightforward to conduct all required assessments physically in …

Despite recent advances in Machine Learning (ML)-based applications for clinical decision
making, there is no objective method that can assist physicians in discriminating clinically …

Background Knee osteoarthritis (OA) remains a leading aetiology of disability worldwide.
Clinical assessment of such knee-related conditions has improved with recent advances in …