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vertebra-column

vertebra-column

active ARFF Publicly available Visibility: public Uploaded 01-06-2015 by Rafael G. Mantovani
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Author: Guilherme de Alencar Barreto, Ajalmar R. da Rocha Neto, Henrique Antonio Fonseca da Mota Filho Source: [original](http://www.openml.org/d/1523) - UCI Please cite: * Dataset Title: Vertebra Column - 2 classes * Abstract: Data set containing values for six biomechanical features used to classify orthopaedic patients into 3 classes (normal, disk hernia or spondilolysthesis) or 2 classes (normal or abnormal). * Source: Guilherme de Alencar Barreto (guilherme '@' deti.ufc.br) & Ajalmar R. da Rocha Neto (ajalmar '@' ifce.edu.br), Department of Teleinformatics Engineering, Federal University of Ceará, Fortaleza, Ceará, Brazil. Henrique Antonio Fonseca da Mota Filho (hdamota '@' gmail.com), Hospital Monte Klinikum, Fortaleza, Ceará, Brazil. * Data Set Information: Biomedical data set built by Dr. Henrique da Mota during a medical residence period in the Group of Applied Research in Orthopaedics (GARO) of the Centre Médico-Chirurgical de Réadaptation des Massues, Lyon, France. The data have been organized in two different but related classification tasks. The first task consists in classifying patients as belonging to one out of three categories: Normal (100 patients), Disk Hernia (60 patients) or Spondylolisthesis (150 patients). For the second task, the categories Disk Hernia and Spondylolisthesis were merged into a single category labelled as 'abnormal'. Thus, the second task consists in classifying patients as belonging to one out of two categories: Normal (100 patients) or Abnormal (210 patients). We provide files also for use within the WEKA environment. * Attribute Information: Each patient is represented in the data set by six biomechanical attributes derived from the shape and orientation of the pelvis and lumbar spine (in this order): pelvic incidence, pelvic tilt, lumbar lordosis angle, sacral slope, pelvic radius and grade of spondylolisthesis. The following convention is used for the class labels: DH (Disk Hernia), Spondylolisthesis (SL), Normal (NO) and Abnormal (AB). * Relevant Papers: (1) Berthonnaud, E., Dimnet, J., Roussouly, P. & Labelle, H. (2005). 'Analysis of the sagittal balance of the spine and pelvis using shape and orientation parameters', Journal of Spinal Disorders & Techniques, 18(1):40–47. (2) Rocha Neto, A. R. & Barreto, G. A. (2009). 'On the Application of Ensembles of Classifiers to the Diagnosis of Pathologies of the Vertebral Column: A Comparative Analysis', IEEE Latin America Transactions, 7(4):487-496. (3) Rocha Neto, A. R., Sousa, R., Barreto, G. A. & Cardoso, J. S. (2011). 'Diagnostic of Pathology on the Vertebral Column with Embedded Reject Option”, Proceedings of the 5th Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA'2011), Gran Canaria, Spain, Lecture Notes on Computer Science, vol. 6669, p. 588-595.

7 features

Class (target)nominal2 unique values
0 missing
V1numeric310 unique values
0 missing
V2numeric310 unique values
0 missing
V3numeric280 unique values
0 missing
V4numeric279 unique values
0 missing
V5numeric310 unique values
0 missing
V6numeric310 unique values
0 missing

62 properties

310
Number of instances (rows) of the dataset.
7
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
6
Number of numeric attributes.
1
Number of nominal attributes.
14.29
Percentage of binary attributes.
15.33
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
0.16
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
38.07
Maximum kurtosis among attributes of the numeric type.
17.54
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
11.77
Third quartile of kurtosis among attributes of the numeric type.
117.92
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
85.71
Percentage of numeric attributes.
74.85
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
14.29
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
2
The maximum number of distinct values among attributes of the nominal type.
-0.18
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
1.67
Third quartile of skewness among attributes of the numeric type.
4.32
Maximum skewness among attributes of the numeric type.
10.01
Minimum standard deviation of attributes of the numeric type.
0.21
First quartile of kurtosis among attributes of the numeric type.
23.31
Third quartile of standard deviation of attributes of the numeric type.
37.56
Maximum standard deviation of attributes of the numeric type.
32.26
Percentage of instances belonging to the least frequent class.
24.11
First quartile of means among attributes of the numeric type.
0
Standard deviation of the number of distinct values among attributes of the nominal type.
Average entropy of the attributes.
100
Number of instances belonging to the least frequent class.
First quartile of mutual information between the nominal attributes and the target attribute.
7.18
Mean kurtosis among attributes of the numeric type.
1
Number of binary attributes.
0.35
First quartile of skewness among attributes of the numeric type.
52.86
Mean of means among attributes of the numeric type.
12.49
First quartile of standard deviation of attributes of the numeric type.
1
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
0.91
Entropy of the target attribute values.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0.81
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.02
Number of attributes divided by the number of instances.
2
Average number of distinct values among the attributes of the nominal type.
47.44
Second quartile (Median) of means among attributes of the numeric type.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
1.12
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
67.74
Percentage of instances belonging to the most frequent class.
18.35
Mean standard deviation of attributes of the numeric type.
0.64
Second quartile (Median) of skewness among attributes of the numeric type.
210
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.

5 tasks

93 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
31 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - target_feature: Class
0 runs - estimation_procedure: 50 times Clustering
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