Clinicians, healthcare providers-suppliers, policy makers and patients are experiencing
exciting opportunities in light of new information deriving from the analysis of big data sets, a …

The use of machine learning techniques in medicine has increased in recent years due to a
rise in publicly available datasets. These techniques have been applied in high blood …

This study proposes an efficient neural network with convolutional layers to classify
significantly class-imbalanced clinical data. The data is curated from the National Health and …

The emergence of powerful software has created conditions and approaches for large
datasets to be collected and analyzed which has led to informed decision-making towards …

Abstract Background and Objective: Patients with End-Stage Kidney Disease (ESKD) have a
unique cardiovascular risk. This study aims at predicting, with a certain precision, death and …

This paper focus on a neural network classification model to estimate the association among
gender, race, BMI, age, smoking, kidney disease and diabetes in hypertensive patients. It …

Big data and artificial intelligence are currently two of the most important and trending pieces
for innovation and predictive analytics in healthcare, leading the digital healthcare …

High blood pressure (BP) or hypertension is a dangerous and deadly condition which can
lead to serious disorders and high risk of heart attacks, strokes or death. Therefore, studying …

High blood pressure early screening remains a challenge due to the lack of symptoms
associated with it. Accordingly, noninvasive methods based on photoplethysmography …

Hypertension is a widespread chronic disease. Risk prediction of hypertension is an
intervention that contributes to the early prevention and management of hypertension. The …