Advancements in computing and data from the near universal acceptance and
implementation of electronic health records has been formative for the growth of …

Parkinson's disease (PD) is characterized by the loss of neuronal cells, which leads to
synaptic dysfunction and cognitive defects. Despite the advancements in treatment …

Objective To evaluate the performance of machine learning (ML) algorithms and to compare
them with logistic regression for the prediction of risk of cardiovascular diseases (CVDs) …

Abstract Machine learning (ML) involves algorithms learning patterns in large, complex
datasets to predict and classify. Algorithms include neural networks (NN), logistic regression …

Background Rehabilitation medicine is facing a new development phase thanks to a recent
wave of rigorous clinical trials aimed at improving the scientific evidence of protocols. This …

Background Despite the expanding use of machine learning (ML) in fields such as finance
and marketing, its application in the daily practice of clinical medicine is almost non-existent …

Abstract Machine learning (ML) is a rapidly advancing field with increasing utility in health
care. We conducted a systematic review and critical appraisal of ML applications in vascular …

Data-driven machine learning (ML) methods are extensively employed for modeling and
simulation of highly complicated processes. ML techniques confirmed their great predictive …

Background and purpose Machine learning (ML) has attracted much attention with the hope
that it could make use of large, routinely collected datasets and deliver accurate …

With the boom in Internet techniques and computer science, a variety of big data have been
introduced into forecasting research, bringing new knowledge and improving prediction …