Stroke prediction based on a random sample of the approximately 6 million patient records from GE Medical Quality Improvement Consortium (MQIC) database.
In the U.K 2.6 million people have diabetes.It can have serious health consequences if not diagnosed immediately. A new improved way of detecting Diabetes which can change lives with this model.
Data UCI
The aim is to distinguish between the presence and absence of cardiac arrhythmia and to classify it in one of the 16 groups:
Class 01 refers to 'normal' ECG
Classes 02 to 15 refers to different classes of arrhythmia
Class 16 refers to the rest of unclassified ones.
For the time being, there exists a computer program that makes such a classification. However there are differences between the cardiolog's and the programs classification. Taking the cardiolog's as a gold standard we aim to minimise this difference by means of machine learning tools.
Model that predicts which percentage of patients will rate a hospital at 9 or 10, based on a dataset of hospital patient surveys. It shows which survey items and which scores are important for a good overall result. The data is from a list of hospital ratings for the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS). HCAHPS is a national, standardized survey of hospital patients about their experiences during a recent inpatient hospital stay. https://data.medicare.gov/dataset/Survey-of-Patients-Hospital-Experiences-HCAHPS-/rj76-22dk
This is one of three domains provided by the Oncology Institute that has repeatedly appeared in the machine learning literature. (See also lymphography and primary-tumor.)
This data set includes 201 instances of one class and 85 instances of another class. The instances are described by 9 attributes, some of which are linear and some are nominal.
