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Statlog (Heart) Data Set
Download: Data Folder, Data Set Description
Abstract: This dataset is a heart disease database similar to a database already present in the repository (Heart Disease databases) but in a slightly different form
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Data Set Characteristics: |
Multivariate |
Number of Instances: |
270 |
Area: |
Life |
Attribute Characteristics: |
Categorical, Real |
Number of Attributes: |
13 |
Date Donated |
N/A |
Associated Tasks: |
Classification |
Missing Values? |
No |
Number of Web Hits: |
306087 |
Source:
N/A
Data Set Information:
Cost Matrix
_______ abse pres
absence 0 1
presence 5 0
where the rows represent the true values and the columns the predicted.
Attribute Information:
Attribute Information:
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-- 1. age
-- 2. sex
-- 3. chest pain type (4 values)
-- 4. resting blood pressure
-- 5. serum cholesterol in mg/dl
-- 6. fasting blood sugar > 120 mg/dl
-- 7. resting electrocardiographic results (values 0,1,2)
-- 8. maximum heart rate achieved
-- 9. exercise induced angina
-- 10. oldpeak = ST depression induced by exercise relative to rest
-- 11. the slope of the peak exercise ST segment
-- 12. number of major vessels (0-3) colored by flourosopy
-- 13. thal: 3 = normal; 6 = fixed defect; 7 = reversable defect
Attributes types
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Real: 1,4,5,8,10,12
Ordered:11,
Binary: 2,6,9
Nominal:7,3,13
Variable to be predicted
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Absence (1) or presence (2) of heart disease
Relevant Papers:
N/A
Papers That Cite This Data Set1:
 Gavin Brown. Diversity in Neural Network Ensembles. The University of Birmingham. 2004. [View Context].
Igor Kononenko and Edvard Simec and Marko Robnik-Sikonja. Overcoming the Myopia of Inductive Learning Algorithms with RELIEFF. Appl. Intell, 7. 1997. [View Context].
Alexander K. Seewald. Dissertation Towards Understanding Stacking Studies of a General Ensemble Learning Scheme ausgefuhrt zum Zwecke der Erlangung des akademischen Grades eines Doktors der technischen Naturwissenschaften. [View Context].
Elena Smirnova and Ida G. Sprinkhuizen-Kuyper and I. Nalbantis and b. ERIM and Universiteit Rotterdam. Unanimous Voting using Support Vector Machines. IKAT, Universiteit Maastricht. [View Context].
Citation Request:
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