Development and internal validation of a machine-learning-developed model for predicting 1-year mortality after fragility hip fracture
Issued Date
2022-12-01
Resource Type
eISSN
14712318
Scopus ID
2-s2.0-85130724924
Pubmed ID
35610589
Journal Title
BMC Geriatrics
Volume
22
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
BMC Geriatrics Vol.22 No.1 (2022)
Suggested Citation
Kitcharanant N., Chotiyarnwong P., Tanphiriyakun T., Vanitcharoenkul E., Mahaisavariya C., Boonyaprapa W., Unnanuntana A. Development and internal validation of a machine-learning-developed model for predicting 1-year mortality after fragility hip fracture. BMC Geriatrics Vol.22 No.1 (2022). doi:10.1186/s12877-022-03152-x Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/85309
Title
Development and internal validation of a machine-learning-developed model for predicting 1-year mortality after fragility hip fracture
Author's Affiliation
Other Contributor(s)
Abstract
Background: Fragility hip fracture increases morbidity and mortality in older adult patients, especially within the first year. Identification of patients at high risk of death facilitates modification of associated perioperative factors that can reduce mortality. Various machine learning algorithms have been developed and are widely used in healthcare research, particularly for mortality prediction. This study aimed to develop and internally validate 7 machine learning models to predict 1-year mortality after fragility hip fracture. Methods: This retrospective study included patients with fragility hip fractures from a single center (Siriraj Hospital, Bangkok, Thailand) from July 2016 to October 2018. A total of 492 patients were enrolled. They were randomly categorized into a training group (344 cases, 70%) or a testing group (148 cases, 30%). Various machine learning techniques were used: the Gradient Boosting Classifier (GB), Random Forests Classifier (RF), Artificial Neural Network Classifier (ANN), Logistic Regression Classifier (LR), Naive Bayes Classifier (NB), Support Vector Machine Classifier (SVM), and K-Nearest Neighbors Classifier (KNN). All models were internally validated by evaluating their performance and the area under a receiver operating characteristic curve (AUC). Results: For the testing dataset, the accuracies were GB model = 0.93, RF model = 0.95, ANN model = 0.94, LR model = 0.91, NB model = 0.89, SVM model = 0.90, and KNN model = 0.90. All models achieved high AUCs that ranged between 0.81 and 0.99. The RF model also provided a negative predictive value of 0.96, a positive predictive value of 0.93, a specificity of 0.99, and a sensitivity of 0.68. Conclusions: Our machine learning approach facilitated the successful development of an accurate model to predict 1-year mortality after fragility hip fracture. Several machine learning algorithms (eg, Gradient Boosting and Random Forest) had the potential to provide high predictive performance based on the clinical parameters of each patient. The web application is available at www.hipprediction.com. External validation in a larger group of patients or in different hospital settings is warranted to evaluate the clinical utility of this tool. Trial registration: Thai Clinical Trials Registry (22 February 2021; reg. no. TCTR20210222003).