GitHub

01-data-wrangling.Rmd

In the original **R Opus**, HGH presented datasets from actual research conducted in her lab, that she worked with throughout. I think this is a great approach, and with her permission we're going to ...
ahajournals.org

Abstract WMP49: A machine learning approach to classifying ischemic stroke etiology using variables available in the Get-with-the-Guidelines Stroke Registry

Background: There is a scarcity of US data about the epidemiology of ischemic stroke etiologies. The stroke etiology data field in the Get-with-the-Guidelines (GWTG) Stroke Registry has a 43% missing ...
Europe PMC

Classifying 2-year recurrence in patients with dlbcl using clinical variables with imbalanced data and machine learning methods.

Either your web browser doesn't support Javascript or it is currently turned off. In the latter case, please turn on Javascript support in your web browser and reload ...
Europe PMC

Using sensor data patterns from an automatic milking system to develop predictive variables for classifying clinical mastitis and abnormal milk

Dairy farmers using automatic milking are able to manage mastitis successfully with the help of mastitis attention lists. These attention lists are generated with mastitis detection models that make ...
BMJ

O-38 A new method for classifying athletes between powerful and endurance: physiological variables that explain their different performance in field tests

1 Department of Physiology, Faculty of Medicine, University of Antioquia, Medellin-Colombia 2 Sports Medicine, University of Antioquia, Faculty of Medicine, Medellin-Colombia 3 Indeportes Antioquia ...
GitHub

Kaggle competition for Classifying forest categories using cartographic variables

In this competition you are asked to predict the forest cover type (the predominant kind of tree cover) from strictly cartographic variables (as opposed to remotely sensed data). The actual forest ...