Data science is likely to lead to major changes in cardiovascular imaging. Problems with
timing, efficiency, and missed diagnoses occur at all stages of the imaging chain. The …

Background How to select variables and identify functional forms for continuous variables is
a key concern when creating a multivariable model. Ad hoc 'traditional'approaches to …

Several machine learning (ML) algorithms have been increasingly utilized for cardiovascular
disease prediction. We aim to assess and summarize the overall predictive ability of ML …

Over the last decade Deep learning-based models surpasses classical machine learning
models in a variety of text classification tasks. The primary challenge with text classification …

Background As more and more researchers are turning to big data for new opportunities of
biomedical discoveries, machine learning models, as the backbone of big data analysis, are …

Stock-market prediction using machine-learning technique aims at developing effective and
efficient models that can provide a better and higher rate of prediction accuracy. Numerous …

This book is about learning from data using the Generalized Additive Models for Location,
Scale and Shape (GAMLSS). GAMLSS extends the Generalized Linear Models (GLMs) and …

There is a large literature explaining why AdaBoost is a successful classifier. The literature
on AdaBoost focuses on classifier margins and boosting's interpretation as the optimization …

Echocardiography plays a crucial role in the diagnosis and management of cardiovascular
disease. However, interpretation remains largely reliant on the subjective expertise of the …

Artificial intelligence (AI) has begun to permeate and reform the field of medicine and
cardiovascular medicine. Impacting about 100 million patients in the United States, the …