Data-Mining and Expert Models for Predicting Injury Risk in Ski Resorts

This paper proposes several models for predicting global daily injury risk in ski resorts. There are three types of models proposed: based on data mining, expert modelling, and a combination of both. We show that the expert model that represents the judgment of injury risk experts in the analyzed ski resort is 10–15 % less accurate than data mining models. We also show that expert models refined with data-driven analysis can produce models that are in line with accuracy as data mining models, but in addition show some advantages, like transparency, consistency and completeness.

[1]  Carlos A. Bana e Costa,et al.  The MACBETH Approach: Basic Ideas, Software, and an Application , 1999 .

[2]  Michael A. King,et al.  Ensemble methods for advanced skier days prediction , 2014, Expert Syst. Appl..

[3]  Blaz Zupan,et al.  Orange: Data Mining Fruitful and Fun - A Historical Perspective , 2013, Informatica.

[4]  Peter Clark,et al.  The CN2 Induction Algorithm , 1989, Machine Learning.

[5]  Marko Bohanec,et al.  A Hierarchical Multi-Attribute Model for Bank Reputational Risk Assessment , 2014, DSS.

[6]  Anita Brandstätter,et al.  Facial trauma: how dangerous are skiing and snowboarding? , 2010, Journal of oral and maxillofacial surgery : official journal of the American Association of Oral and Maxillofacial Surgeons.

[7]  T Harlow,et al.  Factors predisposing to skiing injuries in Britons. , 1996, Injury.

[8]  J. Slauterbeck,et al.  The Effects of Level of Competition, Sport, and Sex on the Incidence of First-Time Noncontact Anterior Cruciate Ligament Injury , 2014, The American journal of sports medicine.

[9]  Farshad Hakimpour,et al.  Prediction of Crash Severity on Two-Lane, Two-Way Roads Based on Fuzzy Classification and Regression Tree Using Geospatial Analysis , 2015 .

[10]  Ivan Bratko,et al.  Testing the significance of attribute interactions , 2004, ICML.

[11]  Salvatore Greco,et al.  Rough sets theory for multicriteria decision analysis , 2001, Eur. J. Oper. Res..

[12]  M. Burtscher,et al.  Factors associated with injuries occurred on slope intersections and in snow parks compared to on-slope injuries. , 2013, Accident; analysis and prevention.

[13]  Alexander I. Mechitov,et al.  Verbal Decision Analysis: Foundations and Trends , 2013, Adv. Decis. Sci..

[14]  Khair Jadaan,et al.  Prediction of Road Traffic Accidents in Jordan using Artificial Neural Network (ANN) , 2014 .

[15]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[16]  Matthias Ehrgott,et al.  Multiple criteria decision analysis: state of the art surveys , 2005 .

[17]  Vladislav Rajkovic,et al.  DEX Methodology: Three Decades of Qualitative Multi-Attribute Modeling , 2013, Informatica.

[18]  Alessio Ishizaka,et al.  Multi-criteria Decision Analysis: Methods and Software , 2013 .

[19]  Martin Mozina,et al.  Orange: data mining toolbox in python , 2013, J. Mach. Learn. Res..

[20]  W J Warme,et al.  Ski Injury Statistics, 1982 to 1993, Jackson Hole Ski Resort , 1995, The American journal of sports medicine.

[21]  John E. Taylor,et al.  Special Issue on Computational Approaches to Understand and Reduce Energy Consumption in the Built Environment , 2014 .

[22]  Olga N. Aneziris,et al.  Risk horoscopes: Predicting the number and type of serious occupational accidents in The Netherlands for sectors and jobs , 2015, Reliab. Eng. Syst. Saf..