Echocardiogram
Donated on 2/27/1989
Data for classifying if patients will survive for at least one year after a heart attack
Dataset Characteristics
Multivariate
Subject Area
Health and Medicine
Associated Tasks
Classification
Feature Type
Categorical, Integer, Real
# Instances
132
# Features
9
Dataset Information
Additional Information
All the patients suffered heart attacks at some point in the past. Some are still alive and some are not. The survival and still-alive variables, when taken together, indicate whether a patient survived for at least one year following the heart attack. The problem addressed by past researchers was to predict from the other variables whether or not the patient will survive at least one year. The most difficult part of this problem is correctly predicting that the patient will NOT survive. (Part of the difficulty seems to be the size of the data set.)
Has Missing Values?
Yes
Variables Table
Variable Name | Role | Type | Description | Units | Missing Values |
---|---|---|---|---|---|
survival | Target | Integer | yes | ||
still-alive | Target | Integer | yes | ||
age-at-heart-attack | Feature | Integer | yes | ||
pericardial-effusion | Feature | Binary | no | ||
fractional-shortening | Feature | Continuous | yes | ||
epss | Feature | Integer | yes | ||
lvdd | Feature | Continuous | yes | ||
wall-motion-score | Feature | Continuous | yes | ||
wall-motion-index | Feature | Continuous | yes | ||
mult | Feature | Continuous | yes |
0 to 10 of 13
Additional Variable Information
1. survival -- the number of months patient survived (has survived, if patient is still alive). Because all the patients had their heart attacks at different times, it is possible that some patients have survived less than one year but they are still alive. Check the second variable to confirm this. Such patients cannot be used for the prediction task mentioned above. 2. still-alive -- a binary variable. 0=dead at end of survival period, 1 means still alive 3. age-at-heart-attack -- age in years when heart attack occurred 4. pericardial-effusion -- binary. Pericardial effusion is fluid around the heart. 0=no fluid, 1=fluid 5. fractional-shortening -- a measure of contracility around the heart lower numbers are increasingly abnormal 6. epss -- E-point septal separation, another measure of contractility. Larger numbers are increasingly abnormal. 7. lvdd -- left ventricular end-diastolic dimension. This is a measure of the size of the heart at end-diastole. Large hearts tend to be sick hearts. 8. wall-motion-score -- a measure of how the segments of the left ventricle are moving 9. wall-motion-index -- equals wall-motion-score divided by number of segments seen. Usually 12-13 segments are seen in an echocardiogram. Use this variable INSTEAD of the wall motion score. 10. mult -- a derivate var which can be ignored 11. name -- the name of the patient (I have replaced them with "name") 12. group -- meaningless, ignore it 13. alive-at-1 -- Boolean-valued. Derived from the first two attributes. 0 means patient was either dead after 1 year or had been followed for less than 1 year. 1 means patient was alive at 1 year.
Dataset Files
File | Size |
---|---|
echocardiogram.data | 6 KB |
echocardiogram.names | 5 KB |
Index | 135 Bytes |
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset echocardiogram = fetch_ucirepo(id=38) # data (as pandas dataframes) X = echocardiogram.data.features y = echocardiogram.data.targets # metadata print(echocardiogram.metadata) # variable information print(echocardiogram.variables)
Echocardiogram [Dataset]. (1988). UCI Machine Learning Repository. https://doi.org/10.24432/C5QW24.
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.