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Open Access Research article

Hip fracture risk assessment: artificial neural network outperforms conditional logistic regression in an age- and sex-matched case control study

Wo-Jan Tseng1, Li-Wei Hung2, Jiann-Shing Shieh3, Maysam F Abbod4 and Jinn Lin2*

Author Affiliations

1 Department of Orthopaedic Surgery, National Taiwan University Hospital Hsin-Chu Branch, No.25, Ln. 442, Sec. 1, Jingguo Rd., East Dist., 300, Hsinchu, Taiwan

2 Department of Orthopaedic Surgery, National Taiwan University Hospital, No.7, Zhongshan S. Rd., Zhongzheng Dist., Taipei, Taiwan

3 Department of Mechanical Engineering, Yuan Ze University, No.135, Yuandong Rd., Zhongli, Taiwan

4 School of Engineering and Design, Brunel University, Kingston LaneUxbridge Middlesex UB8 3PH, West London, United Kingdom

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BMC Musculoskeletal Disorders 2013, 14:207  doi:10.1186/1471-2474-14-207

Published: 15 July 2013

Abstract

Background

Osteoporotic hip fractures with a significant morbidity and excess mortality among the elderly have imposed huge health and economic burdens on societies worldwide. In this age- and sex-matched case control study, we examined the risk factors of hip fractures and assessed the fracture risk by conditional logistic regression (CLR) and ensemble artificial neural network (ANN). The performances of these two classifiers were compared.

Methods

The study population consisted of 217 pairs (149 women and 68 men) of fractures and controls with an age older than 60 years. All the participants were interviewed with the same standardized questionnaire including questions on 66 risk factors in 12 categories. Univariate CLR analysis was initially conducted to examine the unadjusted odds ratio of all potential risk factors. The significant risk factors were then tested by multivariate analyses. For fracture risk assessment, the participants were randomly divided into modeling and testing datasets for 10-fold cross validation analyses. The predicting models built by CLR and ANN in modeling datasets were applied to testing datasets for generalization study. The performances, including discrimination and calibration, were compared with non-parametric Wilcoxon tests.

Results

In univariate CLR analyses, 16 variables achieved significant level, and six of them remained significant in multivariate analyses, including low T score, low BMI, low MMSE score, milk intake, walking difficulty, and significant fall at home. For discrimination, ANN outperformed CLR in both 16- and 6-variable analyses in modeling and testing datasets (p?<?0.005). For calibration, ANN outperformed CLR only in 16-variable analyses in modeling and testing datasets (p?=?0.013 and 0.047, respectively).

Conclusions

The risk factors of hip fracture are more personal than environmental. With adequate model construction, ANN may outperform CLR in both discrimination and calibration. ANN seems to have not been developed to its full potential and efforts should be made to improve its performance.

Keywords:
Hip fracture; Artificial neural network; Conditional logistic regression; Discrimination; Calibration