Classification algorithms for hip fracture prediction based on recursive partitioning methods

This article presents 2 modifications 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...

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Veröffentlicht in:Medical decision making Jg. 24; H. 4; S. 386
Hauptverfasser: Jin, Hua, Lu, Ying, Harris, Steven T, Black, Dennis M, Stone, Katie, Hochberg, Marc C, Genant, Harry K
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States 01.07.2004
Schlagworte:
Absorptiometry, Photon
Aged
Aged, 80 and over
Algorithms
Body Mass Index
Bone Density
Cost-Benefit Analysis
Decision Trees
Female
Hip Fractures - classification
Hip Fractures - economics
Hip Fractures - epidemiology
Humans
Likelihood Functions
Physical Examination
Probability
ISSN:0272-989X
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Zusammenfassung:This article presents 2 modifications 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 algorithm was 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 rule with equivalent diagnostic utility. A 6-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.
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ISSN:0272-989X
DOI:10.1177/0272989X04267009