Ensemble artificial neural networks applied to predict the key risk factors of hip bone fracture for elders. (August 2015)

• Record Type: Journal Article
• Authors: Liu, Quan; Cui, Xingran; Chou, Yuan-Chao; Abbod, Maysam F.; Lin, Jinn; Shieh, Jiann-Shing
• Is Part Of: Biomedical signal processing and control. Volume 21(2015)
• Page Start: 146
• Page End: 156
• Publication Date: 2015-08
• Subjects: Ensemble artificial neural networks -- Back-propagation neural networks -- Sensitivity analysis -- Connection weights -- Hip fracture; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2015.06.002
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: We applied Ensemble ANNs model to predict hip bone fracture risk for both male and female elders. Binary coding genetic algorithm was used to code the raw data. We identified the top 10 risk factors for hip fractures from 74 risk factors. The accuracy of the ANN prediction model is very high. Abstract: Hip bone fracture is one of the most important causes of morbidity and mortality in the elder adults. It is necessary to establish a prediction model to provide suggestions for elders. A total of 725 subjects were involved, including 228 patients with first low-trauma hip fracture and 497 ages-, sex-, and living area-matched controls (215 from the same hospital and 282 from community). All the subjects were interviewed with the same questionnaire, and the answers of the interviewees were recorded to the database. Three-layer back-propagation Artificial Neural Networks (ANN) models were applied for females and males separately in this study to predict the risk of hip bone fracture for elders. Furthermore, to improve the accuracies and the generalizations of the models, the ensemble ANNs method was applied. To understand variables contributions and find the important variables for predicting hip fracture, sensitivity analysis and connection weights approach were applied. In this study, three ANNs prediction models were tested with different architectures. With the fivefold cross-validation method evaluating the performances, one of the three models turned out to be the best prediction model and achieved a big success of prediction. The best area under the receiver operating characteristic (ROC) curve and the accuracy of the prediction model are 0.91 ± 0.028 (mean ± SD) and 0.85 ± 0.029 for females, while for males are 0.99 ± 0.015 and 0.93 ± 0.020. With the method of sensitivity analysis and connection weights, input variables were ranked according to contributions/importance, and the top 10 variables show great proportion of contribution to predict hip fracture. The top 10 important variables causing hip fracture for both females and males are similar to our previous results got from logistic regression model and other related researches. In conclusion, ANNs has successfully been used to establish prediction models for predicting the risk of hip bone fracture for both female and male elder adults respectively and identified the top 10 important variables from 74 input variables to predict hip bone fracture of elders. This study verified the performance of ANNs to be a highly efficient prediction model.