Data mining for occupational injury risk: A case study

International Journal of Reliability, Quality and Safety Engineering

Volumn 17, Issue 4, 2010, Pages 351-380

  Bevilacqua, Maurizio ^a   Ciarapica, Filippo Emanuele ^a   Giacchetta, Giancarlo ^a  

^a UNIVERSITÀ POLITECNICA DELLE MARCHE   (Italy)

Author keywords

classification and regression tree; data mining; neural network; neuro fuzzy techniques; Occupational injury

Indexed keywords

ARTIFICIAL NEURAL NETWORK; AUTOMATIC INTERACTION DETECTION; CHI-SQUARED; CLASSIFICATION AND REGRESSION TREE; CORRECTIVE ACTIONS; DATA MINING TECHNIQUES; DECISION-MAKING RULES; INPUT DATAS; NEGATIVE BINOMIAL REGRESSION; NEURO-FUZZY NETWORK; NEURO-FUZZY TECHNIQUES; NEUROFUZZY SYSTEM; OCCUPATIONAL INJURY; REAL APPLICATIONS; RISK LEVELS; SOFT COMPUTING TOOLS;

DATA MINING; DECISION MAKING; FUZZY SETS; INPUT OUTPUT PROGRAMS; NEURAL NETWORKS; PROBABILITY DENSITY FUNCTION; REGRESSION ANALYSIS; SENSITIVITY ANALYSIS; SOFT COMPUTING; TREES (MATHEMATICS);

OCCUPATIONAL RISKS;

EID: 78249237927     PISSN: 02185393     EISSN: None     Source Type: Journal    
DOI: 10.1142/S021853931000386X     Document Type: Article

References (53)

1. T. J. Larsson and B. Field, The distribution of occupational injury risk in the State of Victoria, Safety Science 40(2002) 419-437. (Pubitemid 34033616)
2. G. S. Smith, M. A. Veazie and K. L. Benjamin, The use of sentinel injury deaths to evaluate the quality of multiple source reporting for occupational injuries, AEP 5(2005) 219-227.
3. J. A. Waller, J. M. Skelly and H. J. Davis, Treated injuries in northern vermont, Accident Analysis & Prevention 27(6) (1995) 819-828.
4. B. S. Roudsari and M. Ghodsi, Occupational injuries in Tehran, Int. J. Care Injured 36(2005) 33-39.
5. G. S. Sorock and T. K. Courtney, Advancing analytic epidemiologic studies of occupational injuries, Safety Science 25(1997) 29-43.
6. J. P. Leigh, J. E. Cone and R. Harrison, Costs of occupational injuries and illnesses in California, Preventive Medicine 32(2001) 393-406.
7. E. A. Biddle and S. M. Marsh, Comparison of two fatal occupational injury surveillnace systems in the United States, Journal of Safety Research 33(2002) 337-354.
8. S. Salminen, Relationship between injuries at work and during leisure time, Accident Analysis & Prevention 37(2005) 373-376.
9. M. Demichela, N. Piccinini and A. Romano, Risk analysis as a basis for safety management system, Journal of Loss Prevention in the Process Industries 17(2004) 179-185.
10. G. S. Sorock, D. A. Lombardi, T. K. Courtney, J. P. Cotnam and M. A. Mittleman, Epidemiology of occupational acute traumatic hand injuries: A literature review, Safety Science 38(2001) 241.
11. T. K. Amell, S. Kumar and B. W. J. Rosser, Ergonomics, loss management, and occupational injury and illness surveillance. Part 2: Injury and illness incident profile. Sample data, International Journal of Industrial Ergonomics 29(2002) 199-210.
12. C. Trontin and S. Bèjean, Prevention of occupational injuries: Moral hazard and complex agency relationships, Safety Science 42(2004) 121-141.
13. M. K. Lindell, Occupational safety and health inspection scores predict rates of workers' lost-time injuries, Accident Analysis & Prevention 29(5) (1997) 563-571.
14. B. Fabiano, F. Curro and R. Pastorino, A study of the relationship between occupational injuries and firm size and type in the Italian industry, Safety Science 42(7) (2004) 587-600.
15. O. Siu, D. R. Phillips and T. Leung, Safety climate and safety performance among construction workers in Hong Kong, The role of psychological strains as mediators, Accident Analysis & Prevention 36(3) (2004) 359-366.
16. P. L. Clemens, When is it time to worry about clustering, Professional Safety (May 2003).
17. K. Wang, A. H. Lee, K. K. W. Yau and P. J. W. Carrivick, A bivariate zero-inflated Poisson regression model to analyze occupational injuries, Accident Analysis & Prevention 35(4) (2003) 625-629.
18. K. K. W. Yau, A. H. Lee and P. J. W. Carrivick, Modeling zero-inflated count series with application to occupational health, Computer M ethods and Programs i n Biomedicine 74(2004) 47-52.
19. R. P. Hertz and E. A. Emmett, Risk factor of occupational hand injury, J. Occ. Med. 28(1986) 36-41.
20. A. Persona, D. Battini, M. Faccio, M. Bevilacqua and F. E. Ciarapica, Classification of occupational injury cases using the regression tree approach, International Journal of Reliability, Quality and Safety Engineering 13(2) (2006) 171-191.
21. L. Mussone, A. Ferrari and M. Oneta, An analysis of urban collisions using an artificial intelligence model, Accident Analysis & Prevention 31(6) (1999) 705-718.
22. S. Sohn and S. Lee, Data fusion, ensemble and clustering to improve the classification accuracy for the severity of road traffic accident in Korea, Safety Science 41(1) (2003) 1-14.
23. M. Abdel-Aty and A. Pande, Identifying crash propensity using traffic speed conditions, Journal of Safety Research 36(1) (2005) 97-108.
24. L. Y. Chang, Analysis of freeway accident frequencies: Negative binomial regression versus artificial neural network, Safety Science 43(2005) 541-557.
25. P. K. Dash, A. C. Liew, S. Rahman and S. Dash, Fuzzy and neuro-fuzzy computing models for electric load forecasting, Engineering Applications of Artificial Intelligence 8(4) (1995) 423-433.
26. A. Bakirtzis, J. Theocharis, S. Kiartzis and K. Satsios, Short term load forecasting using fuzzy neural networks, paper 95 WM 155-2-PWRS presented at the IEEE/PES (1995) Winter Meeting.
27. L. Wilson, Basic learnings in industrial safetyand loss management, Association of Professional Engineers, Geologists and Geophysicists of Alberta (APEGGA) and the Industrial Safet yand Loss Management Program (ISLMP), Faculty of Engineering, University of Alberta, Edmonton, Canada (1998) p. 72.
28. S-RCM Training Guide, Shell-reliability centered maintenance, Shell Global Solution International (2000).
29. K. A. L. Van Heel, B. Knegtering and A. C. Brombacher, Safety lifecycle management. A flowchart presentation of the IEC 61508 overall safety lifecycle model, Quality and Reliability Engineering International 15(6) (1999) 493-500.
30. S-RBI Manual, Shell-risk based inspection, Shell Global Solution International, (2003).
31. L. Breiman, J. Friedman, R. Olshen and C. J. Stone, Classification and Regression Trees and (Belmont, CA: Wadsworth, 1984).
32. G. Kass, An exploratory technique for investigating large quantities of categorical data, Appl. Stat. 29(2) (1980) 119-127.
33. D. B. Biggs, B. de Ville and E. A. Suen, Method of choosing multiway partitions for classification and decision trees, J. Appl. Stat. 18(1) (1991) 49-62.
34. W. Y. Loh and Y. S. Shih, Split selection methods for classification trees, Stat. Sinics 7(1997) 815-840.
35. J. A. Michael and S. L. Gordon, Data Mining Technique: For Marketing, Sales and Customer Support (Wiley, New York, 1997).
36. M. Bevilacqua, M. Braglia and R. Montanari, The classification and regression tree approach to pump failure rate analysis, Reliability Engineering and System Safety 79(1) (2003) 59-67.
37. G. Cybenko, Approximation by superpositions of sigmoidal functions, Math. Control Signals Syst. 2(1989) 303-314.
38. K. Hornik, M. Stinchombe and H. White, Universal approximation of an unknown mapping and its derivative using multilayer feedforward networks, Neural Netw. 3(1990) 551-560.
39. K. Hornik, Approximation capabilities of multilayer feedforward networks, Neural Netw. 4(1991) 251-257.
40. F. Girosi and T. Poggio, Networks for learning, in Neural Networks: Concepts, Applications and Implementation, (eds.) P. Antognetti and V. V. Milutinovic, Prentice Hall, Inc, Englewood Cliffs, NJ (1991), pp. 110-154.
41. M. Leshno, V. Ya. Lin, A. Pinkus and S. Schocken, Multilayer feedforward networks with a non polynomial activation function can approximate any function, Neural Netw. 6(1993) 861-867.
42. L. Y. Chang and W. C. Chen, Data mining of tree-based models to analyze freeway accident frequency, Journal of Safety Research 36(2005) 365-375.
43. T. S. Lee, C. C. Chiu, Y. C. Chou and C. J. Lu, Mining the customer credit using classification and regression tree and multivariate adaptive regression splines, Computational Statistics & Data Analysis 50(4) (2006) 1113-1130.
44. D. Brown, J. R. Stile, L. Gunderson and T. C. Giras, Mining human failure dynamics from accident data using logistic regression and decision tree, Systems, Man and Cybernetics, IEEE, International Conference 6(2002) 6-9.
45. S. Piramuthu, Financial credit-risk evaluation with neural and neurofuzzy systems, European J. Oper. Res. 112(1999) 310-321.
46. M. Ture, I. Kurt, A. T. Kurum and K. Ozdamar, Comparing classification techniques for predicting essential hypertension, Expert Systems with Applications 29(2005) 583-588.
47. S. Chebrolu, A. Abraham and J. P. Thomas, Feature deduction and ensemble design of intrusion detection systems, Computers & Security 24(2005) 295.
48. E. H. Sorensen, K. L. Miller and C. K. Ooi, The decision tree approach to stock selection, J. Portfolio Management 27(2000) 42.
49. F. Berzal, J. C. Cubero, F. Cuenca and M. J. Martin Bautista, On the quest for easy-to-understand splitting rules, Data & Knowledge Engineering 44(2003) 31-48.
50. J. Leigh, P. Macaskill, E. Kousoma and J. Mandryk, Global burden of disease and injury due to occupational factors, Epidemiology 10(1999) 626.
51. H. Y. Tong and W. T. Hung, Neural network modeling of vehicle discharge headway at signalized intersection: Model descriptions and the results, Transportation Research Part A 36(1) (2002) 17-40.
52. T. Tagaki and M. Sugeno, Fuzzy identification of systems and its application to modelling and control, IEEE Transactions on Systems, Men and Cybernetics 15(1985) 116-132.
53. M. Sugeno and G. T. Kang, Structure identification of fuzzy model, Fuzzy Sets and Systems 28(1988) 15-33.