Task:
Your task is to assist a doctor in predicting whether or not a patient has heart disease (specifically myocardial perfusion diagnosis). Your prediction will be based on partial diagnosis made on images of different parts of the heart.

Values:
A heart is scanned and pictures of 2D images are generated for five different parts of the heart:
Area A: Near the heart's apex (4 ROIs)
Area B: In middle of the "LV" (5 ROIs)
Area C: Near the heart base (5 ROIs)
Area D: In the center of the LV cavity for horizontal long axis view (4 ROIs)
Area E: In the center of the LV cavity for vertical long axis view (4 ROIs)
There are 4 or 5 regions of interest (ROIs) in each image. 

A cardiologist makes a partial diagnoses for each of the 22 Regions of Interest (ROIs). These partial diagnosis were mechanical to generate and could be performed by a trained nurse. 0 is a negative diagnosis (healthy), 1 is a positive diagnosis (unhealthy).  

Column meaning:
Each column represents a partial diagnosis by a cardiologist for a particular ROI:
Column index, Area.ROI 
1, A.1
2, A.2
3, A.3
4, A.4
5, B.1
6, B.2
7, B.3
8, B.4
9, B.5
10, C.1
11, C.2
12, C.3
13, C.4
14, C.5
15, D.1
16, D.2
17, D.3
18, D.4
19, E.1
20, E.2
21, E.3
22, E.4
See figure 3 in the paper "Knowledge discovery approach to automated cardiac SPECT diagnosis" by Kurgan et Al for a visual.

Prediction:
The variable you are predicting is the overall diagnosis of heart disease. The labels were generated by a team of cardiologists based on detailed analysis of each case.

Credit:
This dataset was collected by Kurgan et al and is hosted by the UCI Machine Learning Repository:
http://archive.ics.uci.edu/ml/datasets/SPECT+Heart

