Data Set Information:

根据研究目的，该数据集允许属性和属性排除的几种新组合，或修改属性类型（分类、整数或实数）。该数据集（工作缺勤-第一部分）曾在诺维德朱略大学信息学和知识管理研究生课程的学术研究中使用。

Attribute Information:

1. Individual identification (ID)
2. Reason for absence (ICD).
Absences attested by the International Code of Diseases (ICD) stratified into 21 categories (I to XXI) as follows:

I Certain infectious and parasitic diseases  
II Neoplasms  
III Diseases of the blood and blood-forming organs and certain disorders involving the immune mechanism  
IV Endocrine, nutritional and metabolic diseases  
V Mental and behavioural disorders  
VI Diseases of the nervous system  
VII Diseases of the eye and adnexa  
VIII Diseases of the ear and mastoid process  
IX Diseases of the circulatory system  
X Diseases of the respiratory system  
XI Diseases of the digestive system  
XII Diseases of the skin and subcutaneous tissue  
XIII Diseases of the musculoskeletal system and connective tissue  
XIV Diseases of the genitourinary system  
XV Pregnancy, childbirth and the puerperium  
XVI Certain conditions originating in the perinatal period  
XVII Congenital malformations, deformations and chromosomal abnormalities  
XVIII Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified  
XIX Injury, poisoning and certain other consequences of external causes  
XX External causes of morbidity and mortality  
XXI Factors influencing health status and contact with health services.

And 7 categories without (CID) patient follow-up (22), medical consultation (23), blood donation (24), laboratory examination (25), unjustified absence (26), physiotherapy (27), dental consultation (28).
3. Month of absence
4. Day of the week (Monday (2), Tuesday (3), Wednesday (4), Thursday (5), Friday (6))
5. Seasons (summer (1), autumn (2), winter (3), spring (4))
6. Transportation expense
7. Distance from Residence to Work (kilometers)
8. Service time
9. Age
10. Work load Average/day
11. Hit target
12. Disciplinary failure (yes=1; no=0)
13. Education (high school (1), graduate (2), postgraduate (3), master and doctor (4))
14. Son (number of children)
15. Social drinker (yes=1; no=0)
16. Social smoker (yes=1; no=0)
17. Pet (number of pet)
18. Weight
19. Height
20. Body mass index
21. Absenteeism time in hours (target).arff header for Weka:

@relation Absenteeism_at_work
@attribute ID {31.0, 27.0, 19.0, 30.0, 7.0, 20.0, 24.0, 32.0, 3.0, 33.0, 26.0, 29.0, 18.0, 25.0, 17.0, 14.0, 16.0, 23.0, 2.0, 21.0, 36.0, 15.0, 22.0, 5.0, 12.0, 9.0, 6.0, 34.0, 10.0, 28.0, 13.0, 11.0, 1.0, 4.0, 8.0, 35.0}
@attribute Reason_for_absence {17.0, 3.0, 15.0, 4.0, 21.0, 2.0, 9.0, 24.0, 18.0, 1.0, 12.0, 5.0, 16.0, 7.0, 27.0, 25.0, 8.0, 10.0, 26.0, 19.0, 28.0, 6.0, 23.0, 22.0, 13.0, 14.0, 11.0, 0.0}
@attribute Month_of_absence REAL
@attribute Day_of_the_week {5.0, 2.0, 3.0, 4.0, 6.0}
@attribute Seasons {4.0, 1.0, 2.0, 3.0}
@attribute Transportation_expense REAL
@attribute Distance_from_Residence_to_Work REAL
@attribute Service_time INTEGER
@attribute Age INTEGER
@attribute Work_load_Average/day_ REAL
@attribute Hit_target REAL
@attribute Disciplinary_failure {1.0, 0.0}
@attribute Education REAL
@attribute Son REAL
@attribute Social_drinker {1.0, 0.0}
@attribute Social_smoker {1.0, 0.0}
@attribute Pet REAL
@attribute Weight REAL
@attribute Height REAL
@attribute Body_mass_index REAL
@attribute Absenteeism_time_in_hours REAL

Relevant Papers:

Martiniano, A., Ferreira, R. P., Sassi, R. J., & Affonso, C. (2012). Application of a neuro fuzzy network in prediction of absenteeism at work. In Information Systems and Technologies (CISTI), 7th Iberian Conference on (pp. 1-4). IEEE.

Citation Request:

Martiniano, A., Ferreira, R. P., Sassi, R. J., & Affonso, C. (2012). Application of a neuro fuzzy network in prediction of absenteeism at work. In Information Systems and Technologies (CISTI), 7th Iberian Conference on (pp. 1-4). IEEE.