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Classification Algorithms for Hip Fracture Prediction Based on Recursive Partitioning Methods

Department of Radiology University of California, San Francisco; Department of Mathematics, South China Normal University, Guangzhou, China

Ying Lu, PhD

Department of Radiology, Box 0946, University of California, San Francisco, San Francisco CA 94143-0946ying.lu{at}radiologyucsf.edu

Steven T. Harris, MD

Osteoporosis and Arthritis Research Group,University of California, San Francisco

Dennis M. Black, PhD

Department of Epidemiology and Biostatistics, University of California, San Francisco

Katie Stone, PhD

Department of Epidemiology and Biostatistics, University of California, San Francisco

Marc C. Hochberg, MD, MPH

Department of Medicine, University of Maryland, Baltimore

Harry K. Genant, MD

Department of Radiology, Osteoporosis and Arthritis Research Group, University of California, San Francisco

This article presents 2modifications to the classification and regression tree. The authors improved the robustness of a split in the test sample approach and developed a cost-saving classification algorithm by selecting noninferior to the optimum splits from variables with lower cost or being used in parent splits. The new algorithmwas illustrated by 43 predictive variables for 5-year hip fracture previously documented in the Study of Osteoporotic Fractures. The authors generated the robust optimum classification rule without consideration of classification variable costs and then generated an alternative cost-saving rulewith equivalent diagnostic utility. A6-fold cross-validation study proved that the cost-saving alternative classification is statistically noninferior to the optimal one. Their modified classification and regression tree algorithm can be useful in clinical applications. A dual X-ray absorptiometry hip scan and information from clinical examinations can identify subjects with elevated 5-year hip fracture risk without loss of efficiency to more costly and complicated algorithms.

Key Words: cost-effective prediction • consistency • classification tree • equivalent diagnosis

Medical Decision Making, Vol. 24, No. 4, 386-398 (2004)
DOI: 10.1177/0272989X04267009


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