Abstract: This Study will explore how the IoT and machine learning predict heart disease risks through real-time wearable device and sensor data. The Cleveland and Hungarian datasets have relevant ...
Syncope prediction during head-up tilt testing (HUTT) remains challenging due to the complex interplay between autonomic and cardiovascular responses. This study investigates three computational ...
A machine-learning model based on Transformer architecture, a form of artificial intelligence originally developed for ...
Smartphone apps, fitness trackers and wearable devices help people with heart disease get more physical activity in their ...
It is a common misperception that electrocardiograms (ECGs) simply contain data about heart activity. However, modern ECGs ...
Objectives To evaluate the prognostic value of carotid-femoral pulse wave velocity (cfPWV), a marker of aortic stiffness, for ...
Abstract: Coronary heart disease is a significant global health issue that contributes to high morbidity and mortality rates, including in Indonesia. Public awareness of risk factors for heart disease ...
Customer stories Events & webinars Ebooks & reports Business insights GitHub Skills ...
Objective To develop and validate machine learning (ML)-based models to predict lymph node metastasis (LNM) in patients with gastric cancer (GC). Design Retrospective cohort study. Setting Second ...
Customer stories Events & webinars Ebooks & reports Business insights GitHub Skills ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results