Proceedings of the 2012 Industrial and Systems Engineering Research Conference G. Lim and J.W. Herrmann, eds. Prognostic Analysis of Hip Fracture in Elderly Women with Data Mining Methods Jongsawas Chongwatpol Management Science and Information System, Oklahoma State University, Stillwater, OK 74078, USA Akkarapol Sa-ngasoongsong Industrial Engineering and Management, Oklahoma State University, Stillwater, OK 74078, USA Patarawan Woratanarat, Paphon Sa-ngasoongsong Department of Orthopaedics, Faculty of Medicine, Ramathibodi Hospital Bangkok, Thailand 10400 Abstract Hip fracture has become one of the most public health concerns because it is an increasing cause of significant morbidity, mortality, and costs associated with osteoporosis. Although many studies claimed physical activity is the leading cause of hip fracture, other risk factors should be investigated. Most studies in this area employ either descriptive statistical analysis or traditional regression techniques to assess the association between hip fracture and small sets of clinical risk factors. Consequently, not only many potentially important variables such as pre-fracture health conditions are neglected in their analysis, but the results produced by such regression models may not represent relevant risk factors and pattern recognition of hip fracture appropriately. Thus, in this study, we examine whether more complex analytical models using several data mining techniques can better predict and explain the causes of increasing hip fracture in elderly patients. These techniques including logistic regression model, decision tree, and artificial neural network (ANN) are effective ways to analyze data sets with multiple predictor variables, which include both clinical and non-clinical-related risk factors. The preliminary analysis results show that physical activity, traditional medicine, race, BMI, underlying cerebrovascular disease, and alcohol consumption are among the key risk factors. Keywords Hip fracture, Data Mining, Artificial Neural Network, Logistic Regression, 1. Introduction Hip fracture has become one of the most public health concerns because it is an increasing cause of significant morbidity, mortality, and costs associated with osteoporosis [1]. Since the prevalence of hip fracture incidence increases with age, the burden of hip fracture is expected to rise over the next few decades due to population aging [2, 3]. According to the American Academy of Orthopaedic Surgeons, the number of people who have been hospitalized for hip fracture are estimated to be more than 353,000 people each year and are expected to reach 650,000 by 2050 [4]. The excess mortality rate following hip fracture is observed and estimated at 11-23% at 6 months and 22-29% at 12 months from injury [5, 6].