Data Set Information:

数据集描述了心脏单质子发射计算机断层扫描（SPECT）图像的诊断。每个患者分为两类：正常和异常。对267个SPECT图像集（患者）的数据库进行处理，以提取总结原始SPECT图像的特征。结果，为每个患者创建了44个连续特征模式。对该模式进行进一步处理，得到22个二值特征模式。使用CLIP3算法从这些模式生成分类规则。CLIP3算法生成的规则准确率为84.0%（与心脏病专家的诊断相比）。

SPECT是测试ML算法的良好数据集；它有267个实例，由23个二进制属性描述：

Attribute 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)

Relevant Papers:

Kurgan, L.A., Cios, K.J., Tadeusiewicz, R., Ogiela, M. & Goodenday, L.S. "Knowledge Discovery Approach to Automated Cardiac SPECT Diagnosis" Artificial Intelligence in Medicine, vol. 23:2, pp 149-169, Oct 2001
[Web link]

Cios, K.J., Wedding, D.K. & Liu, N. CLIP3: cover learning using integer programming. Kybernetes, 26:4-5, pp 513-536, 1997

Cios K. J. & Kurgan L. Hybrid Inductive Machine Learning: An Overview of CLIP Algorithms,  In: Jain L.C., and Kacprzyk J. (Eds). New Learning Paradigms in Soft Computing, Physica-Verlag (Springer), 2001
[Web link]

Papers That Cite This Data Set¹:

Rich Caruana and Alexandru Niculescu-Mizil. An Empirical evaluation of Supervised Learning for ROC Area. ROCAI. 2004.  [View Context].

Michael G. Madden. evaluation of the Performance of the Markov Blanket Bayesian Classifier Algorithm. CoRR, csLG/0211003. 2002.  [View Context].

Lukasz A. Kurgan and Waldemar Swiercz and Krzysztof J. Cios. Semantic Mapping of XML Tags Using Inductive Machine Learning. ICMLA. 2002.  [View Context].

M. A. Galway and Michael G. Madden. DEPARTMENT OF INFORMATION TECHNOLOGY technical report NUIG-IT-011002 evaluation of the Performance of the Markov Blanket Bayesian Classifier Algorithm. Department of Information Technology National University of Ireland, Galway.  [View Context].

Citation Request:

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Original Owners:

Krzysztof J. Cios, Lukasz A. Kurgan
University of Colorado at Denver, Denver, CO 80217, U.S.A.
Krys.Cios '@' cudenver.edu
Lucy S. Goodenday
Medical College of Ohio, OH, U.S.A.

Donors:

Lukasz A.Kurgan, Krzysztof J. Cios