Data
SCADI

SCADI

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Author: S.M.M. Fatemi Bushehri,Moslem Dehghanizadeh,Shokoofeh Kalantar,Mohsen Sardari Zarchi Source: UCI - 1994 Please cite: See BibTeX citation SCADI dataset This dataset contains 206 attributes of 70 children with physical and motor disability based on ICF-CY. In particular, the SCADI dataset is the only one that has been used by ML researchers for self-care problems classification based on ICF-CY to this date. The 'Class' field refers to the presence of the self-care problems of the children with physical and motor disabilities.The classes are determined by occupational therapists. The names and social security numbers of the children were recently removed from the dataset. Two files have been 'processed', SCADI.arff for using in WEKA and SCADI.CSV for using in MATLAB and similar tools. ### Attribute information: 1. gender: gender (1 = male; 0 = female) 2. age: age in years 3-205. self-care activities based on ICF-CY (1 = The case has this feature; 0 = otherwise) 206. Classes ( class1 = Caring for body parts problem; class2 = Toileting problem; class3 = Dressing problem; class4 = Washing oneself and Caring for body parts and Dressing problem; class5 = Washing oneself, Caring for body parts, Toileting, and Dressing problem; class6 = Eating, Drinking, Washing oneself, Caring for body parts, toileting,Dressing, Looking after health and Looking after safety problem; class7 = No Problem; )

206 features

Gendernominal2 unique values
0 missing
Agenumeric14 unique values
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d 5100-0nominal2 unique values
0 missing
d 5100-1nominal2 unique values
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d 5100-2nominal2 unique values
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d 5100-3nominal2 unique values
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d 5100-4nominal2 unique values
0 missing
d 5100-8nominal1 unique values
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d 5100-9nominal1 unique values
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d 5101-0nominal1 unique values
0 missing
d 5101-1nominal2 unique values
0 missing
d 5101-2nominal2 unique values
0 missing
d 5101-3nominal2 unique values
0 missing
d 5101-4nominal2 unique values
0 missing
d 5101-8nominal1 unique values
0 missing
d 5101-9nominal1 unique values
0 missing
d 5102-0nominal2 unique values
0 missing
d 5102-1nominal2 unique values
0 missing
d 5102-2nominal2 unique values
0 missing
d 5102-3nominal2 unique values
0 missing
d 5102-4nominal2 unique values
0 missing
d 5102-8nominal1 unique values
0 missing
d 5102-9nominal1 unique values
0 missing
d 5200-0nominal2 unique values
0 missing
d 5200-1nominal2 unique values
0 missing
d 5200-2nominal2 unique values
0 missing
d 5200-3nominal2 unique values
0 missing
d 5200-4nominal2 unique values
0 missing
d 5200-8nominal1 unique values
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d 5200-9nominal1 unique values
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d 5201-0nominal2 unique values
0 missing
d 5201-1nominal2 unique values
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d 5201-2nominal2 unique values
0 missing
d 5201-3nominal2 unique values
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d 5201-4nominal2 unique values
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d 5201-8nominal1 unique values
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d 5201-9nominal1 unique values
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d 5202-0nominal2 unique values
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d 5202-1nominal2 unique values
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d 5202-2nominal2 unique values
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d 5202-3nominal2 unique values
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d 5202-4nominal2 unique values
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d 5202-8nominal1 unique values
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d 5202-9nominal1 unique values
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d 5203-0nominal2 unique values
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d 5203-1nominal2 unique values
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d 5203-2nominal2 unique values
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d 5203-3nominal2 unique values
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d 5203-4nominal2 unique values
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d 5203-8nominal1 unique values
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d 5204-0nominal2 unique values
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d 5204-1nominal2 unique values
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d 5204-3nominal2 unique values
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d 5204-4nominal2 unique values
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d 5204-8nominal1 unique values
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d 5205-1nominal2 unique values
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d 5205-3nominal2 unique values
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d 53001-8nominal1 unique values
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d 5302-2nominal2 unique values
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d 5401-0nominal2 unique values
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d 5401-3nominal2 unique values
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d 5401-8nominal1 unique values
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d 5403-0nominal2 unique values
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d 5403-2nominal2 unique values
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d 5403-3nominal2 unique values
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d 5403-4nominal2 unique values
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d 5403-8nominal1 unique values
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d 5602-0nominal2 unique values
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d 5700-1nominal2 unique values
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d 5700-2nominal2 unique values
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d 5700-3nominal2 unique values
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d 5700-8nominal1 unique values
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d 5700-9nominal1 unique values
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d 5701-0nominal2 unique values
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d 5701-1nominal2 unique values
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d 5701-2nominal2 unique values
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d 5701-3nominal2 unique values
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d 5701-4nominal2 unique values
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d 5701-8nominal1 unique values
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d 5701-9nominal1 unique values
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d 57020-0nominal2 unique values
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d 57020-1nominal2 unique values
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d 57020-2nominal2 unique values
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d 57020-3nominal2 unique values
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d 57020-4nominal2 unique values
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d 57020-8nominal1 unique values
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d 57021-0nominal2 unique values
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d 57021-1nominal2 unique values
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d 57021-3nominal2 unique values
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d 57021-4nominal2 unique values
0 missing
d 57021-5nominal1 unique values
0 missing
d 57021-6nominal1 unique values
0 missing
d 57022-0nominal2 unique values
0 missing
d 57022-1nominal2 unique values
0 missing
d 57022-2nominal2 unique values
0 missing
d 57022-3nominal2 unique values
0 missing
d 57022-4nominal2 unique values
0 missing
d 57022-8nominal1 unique values
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d 57022-9nominal1 unique values
0 missing
d 571-0nominal2 unique values
0 missing
d 571-1nominal2 unique values
0 missing
d 571-2nominal2 unique values
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d 571-3nominal2 unique values
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d 571-4nominal2 unique values
0 missing
d 571-8nominal1 unique values
0 missing
d 571-9nominal1 unique values
0 missing
Classesnominal7 unique values
0 missing

19 properties

70
Number of instances (rows) of the dataset.
206
Number of attributes (columns) of the dataset.
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.
1
Number of numeric attributes.
205
Number of nominal attributes.
Number of instances belonging to the least frequent class.
204
Number of binary attributes.
99.03
Percentage of binary attributes.
0
Percentage of instances having missing values.
Average class difference between consecutive instances.
0
Percentage of missing values.
2.94
Number of attributes divided by the number of instances.
0.49
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
99.51
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.

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