The Cuff-Less Blood Pressure Estimation Dataset [2] from the UCI Machine Learning Repository. It is a subset of the MIMIC-II Waveform Dataset that contains 12000 records of simultaneous PPG and ABP from 942 patients with a sampling rate of 125 Hz. The 12000 records were uniformly split into four parts with 3000 records each. However, as the subject information is lacking, the Hold-one-out strategy was utilized to generate training, validation, and test sets once the data was preprocessed. In the end, the UCI dataset had 291,078 segments, which was around 404 hours of recording, making it substantially the biggest data set with a considerably higher ratio of continuous segments per record (32.15).

[2] Kachuee, M., Kiani, M. M., Mohammadzade, H. & Shabany, M. Cuff-less blood pressure estimation data set (2015). UCI repository https://archive.ics.uci.edu/ml/datasets/Cuff-Less+Blood+Pressure+Estimation.

MIMIC-III is a large, freely-available database comprising deidentified health-related data associated with over forty thousand patients who stayed in critical care units of the Beth Israel Deaconess Medical Center between 2001 and 2012. The database includes information such as demographics, vital sign measurements made at the bedside (~1 data point per hour), laboratory test results, procedures, medications, caregiver notes, imaging reports, and mortality (including post-hospital discharge).MIMIC supports a diverse range of analytic studies spanning epidemiology, clinical decision-rule improvement, and electronic tool development. It is notable for three factors: it is freely available to researchers worldwide; it encompasses a diverse and very large population of ICU patients; and it contains highly granular data, including vital signs, laboratory results, and medications.

Context

Breast Cancer Wisconsin (Diagnostic) Data Set

Content

Data Set Characteristics:

:Number of Instances: 569

:Number of Attributes: 30 numeric, predictive attributes and the class

:Attribute Information:
  - radius (mean of distances from center to points on the perimeter)
  - texture (standard deviation of gray-scale values)
  - perimeter
  - area
  - smoothness (local variation in radius lengths)
  - compactness (perimeter^2 / area - 1.0)
  - concavity (severity of concave portions of the contour)
  - concave points (number of concave portions of the contour)
  - symmetry 
  - fractal dimension ("coastline approximation" - 1)

  The mean, standard error, and "worst" or largest (mean of the three
  largest values) of these features were computed for each image,
  resulting in 30 features. For instance, field 3 is Mean Radius, field
  13 is Radius SE, field 23 is Worst Radius.

  - class:
      - WDBC-Malignant
      - WDBC-Benign

Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image.

Acknowledgements

Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science. This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets. https://goo.gl/U2Uwz2