SPECT Heart
Donated on 9/30/2001
Data on cardiac Single Proton Emission Computed Tomography (SPECT) images. Each patient classified into two categories: normal and abnormal.
Dataset Characteristics
Multivariate
Subject Area
Health and Medicine
Associated Tasks
Classification
Feature Type
Categorical
# Instances
267
# Features
22
Dataset Information
Additional Information
The dataset describes diagnosing of cardiac Single Proton Emission Computed Tomography (SPECT) images. Each of the patients is classified into two categories: normal and abnormal. The database of 267 SPECT image sets (patients) was processed to extract features that summarize the original SPECT images. As a result, 44 continuous feature pattern was created for each patient. The pattern was further processed to obtain 22 binary feature patterns. The CLIP3 algorithm was used to generate classification rules from these patterns. The CLIP3 algorithm generated rules that were 84.0% accurate (as compared with cardilogists' diagnoses). SPECT is a good data set for testing ML algorithms; it has 267 instances that are descibed by 23 binary attributes
Has Missing Values?
No
Introductory Paper
By Lukasz Kurgan, K. Cios, R. Tadeusiewicz, M. Ogiela, L. S. Goodenday. 2001
Published in Artif. Intell. Medicine
Variables Table
| Variable Name | Role | Type | Description | Units | Missing Values |
|---|---|---|---|---|---|
| OVERALL_DIAGNOSIS | Target | Binary | no | ||
| F1 | Feature | Binary | no | ||
| F2 | Feature | Binary | no | ||
| F3 | Feature | Binary | no | ||
| F4 | Feature | Binary | no | ||
| F5 | Feature | Binary | no | ||
| F6 | Feature | Binary | no | ||
| F7 | Feature | Binary | no | ||
| F8 | Feature | Binary | no | ||
| F9 | Feature | Binary | no |
0 to 10 of 23
Additional Variable Information
1. OVERALL_DIAGNOSIS: 0,1 (class attribute, binary) 2. F1: 0,1 (the partial diagnosis 1, binary) 3. F2: 0,1 (the partial diagnosis 2, binary) 4. F3: 0,1 (the partial diagnosis 3, binary) 5. F4: 0,1 (the partial diagnosis 4, binary) 6. F5: 0,1 (the partial diagnosis 5, binary) 7. F6: 0,1 (the partial diagnosis 6, binary) 8. F7: 0,1 (the partial diagnosis 7, binary) 9. F8: 0,1 (the partial diagnosis 8, binary) 10. F9: 0,1 (the partial diagnosis 9, binary) 11. F10: 0,1 (the partial diagnosis 10, binary) 12. F11: 0,1 (the partial diagnosis 11, binary) 13. F12: 0,1 (the partial diagnosis 12, binary) 14. F13: 0,1 (the partial diagnosis 13, binary) 15. F14: 0,1 (the partial diagnosis 14, binary) 16. F15: 0,1 (the partial diagnosis 15, binary) 17. F16: 0,1 (the partial diagnosis 16, binary) 18. F17: 0,1 (the partial diagnosis 17, binary) 19. F18: 0,1 (the partial diagnosis 18, binary) 20. F19: 0,1 (the partial diagnosis 19, binary) 21. F20: 0,1 (the partial diagnosis 20, binary) 22. F21: 0,1 (the partial diagnosis 21, binary) 23. F22: 0,1 (the partial diagnosis 22, binary) - dataset is divided into: -- training data ("SPECT.train" 80 instances) -- testing data ("SPECT.test" 187 instances)
Dataset Files
| File | Size |
|---|---|
| SPECT.test | 8.6 KB |
| SPECT.train | 3.7 KB |
| SPECT.names | 3.6 KB |
| DonorNote.txt | 370 Bytes |
Papers Citing this Dataset
Sort by Year, desc
By Ursula Neumann, Nikita Genze, Dominik Heider. 2017
Published in BioData mining.
By Bogdan Kulynych, Carmela Troncoso. 2017
Published in ArXiv.
By Ursula Neumann, Mona Riemenschneider, Jan-Peter Sowa, Theodor Baars, Julia Kälsch, Ali Canbay, Dominik Heider. 2016
Published in BioData mining.
By Sergei Kuznetsov, Artem Revenko. 2014
Published in ArXiv.
By Ronei Moraes, Liliane Machado, Henri Prade, Gilles Richard. 2013
Published in CIARP.
0 to 5 of 8
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset spect_heart = fetch_ucirepo(id=95) # data (as pandas dataframes) X = spect_heart.data.features y = spect_heart.data.targets # metadata print(spect_heart.metadata) # variable information print(spect_heart.variables)
Cios, K., Kurgan, L., & Goodenday, L. (2001). SPECT Heart [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5P304.
Keywords
Creators
Krzysztof Cios
Lukasz Kurgan
Lucy Goodenday
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.