A SEMI-SUPERVISED MACHINE LEARNING PIPELINE FOR CARDIAC RISK STRATIFICATION USING ECHOCARDIOGRAPHIC VARIABLES
Sign Up to like & getrecommendations! Published in 2019 at "Journal of the American College of Cardiology"
DOI: 10.1016/s0735-1097(19)32047-9
Abstract: The current guidelines for echocardiographic assessment of left ventricular diastolic dysfunction (LVDD) utilize decision trees with rigid recursive dichotomizing rules, leading to overlapping and indeterminate outcomes. We investigated a novel machine-learning pipeline to integrate complex… read more here.
Keywords: semi supervised; supervised machine; machine learning; learning pipeline ... See more keywords
Using machine learning pipeline to predict entry into the attack zone in football
Sign Up to like & getrecommendations! Published in 2023 at "PLOS ONE"
DOI: 10.1371/journal.pone.0265372
Abstract: Sports sciences are increasingly data-intensive nowadays since computational tools can extract information from large amounts of data and derive insights from athlete performances during the competition. This paper addresses a performance prediction problem in soccer,… read more here.
Keywords: using machine; pipeline predict; machine learning; soccer ... See more keywords
An Explainable Machine Learning Pipeline for Stroke Prediction on Imbalanced Data
Sign Up to like & getrecommendations! Published in 2022 at "Diagnostics"
DOI: 10.3390/diagnostics12102392
Abstract: Stroke is an acute neurological dysfunction attributed to a focal injury of the central nervous system due to reduced blood flow to the brain. Nowadays, stroke is a global threat associated with premature death and… read more here.
Keywords: machine learning; explainable machine; prediction; stroke ... See more keywords
Self-Learning Pipeline for Low-Energy Resource-Constrained Devices
Sign Up to like & getrecommendations! Published in 2021 at "Energies"
DOI: 10.3390/en14206636
Abstract: The trend of bringing machine learning (ML) to the Internet of Things (IoT) field devices is becoming ever more relevant, also reducing the overall energy need of the applications. ML models are usually trained in… read more here.
Keywords: learning pipeline; resource constrained; self learning; energy ... See more keywords