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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

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:

252662


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:
------------------------
-- 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
-----------------

Real: 1,4,5,8,10,12
Ordered:11,
Binary: 2,6,9
Nominal:7,3,13

Variable to be predicted
------------------------
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:

Please refer to the Machine Learning Repository's citation policy


[1] Papers were automatically harvested and associated with this data set, in collaboration with Rexa.info

Supported By:

 In Collaboration With:

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