Ann Oper Res DOI 10.1007/s10479-017-2489-0 DATA MINING AND ANALYTICS Predicting pediatric clinic no-shows: a decision analytic framework using elastic net and Bayesian belief network Kazim Topuz 1 · Hasmet Uner 2 · Asil Oztekin 3 · Mehmet Bayram Yildirim 1 © Springer Science+Business Media New York 2017 Abstract No-shows are becoming a major problem in primary care facilities, creating addi- tional costs for the facility while adversely affecting the quality of patient care. Accurately predicting no-shows plays an important role in the overbooking strategy. In this study, a hybrid probabilistic prediction framework based on the elastic net (EN) variable-selection methodology integrated with probabilistic Bayesian Belief Network (BBN) is proposed. The study predicts the “no-show probability of the patient(s)” using demographics, socioeco- nomic status, current appointment information, and appointment attendance history of the patient and the family. The proposed framework is validated using ten years of local pedi- atric clinic data. It is shown that this EN-based BBN framework is a comparable prediction methodology regarding the best approaches found in the literature. More importantly, this methodology provides novel information on the interrelations of predictors and the condi- tional probability of predicting “no-shows.” The output of the model can be applied to the appointment scheduling system for a robust overbooking strategy. Keywords No-show prediction · Elastic net · Bayesian belief networks · Healthcare analytics 1 Introduction No-shows and late cancellations adversely affect health care scheduling systems. The term “no-show” can be defined as a patient who misses an appointment or does not show up at the B Mehmet Bayram Yildirim bayram.yildirim@wichita.edu 1 Department of Industrial and Manufacturing Engineering, Wichita State University, 1845 Fairmount St., Wichita, KS 67260, USA 2 Department of Pediatrics, Kansas University School of Medicine, Wichita, KS, USA 3 Operations and Information Systems, Biomedical Engineering and Biotechnology Program, University of Massachusetts Lowell, Lowell, MA, USA 123