Ensemble-learning approach improves fracture prediction using genomic and phenotypic data

被引:3
作者
Wu, Qing [1 ]
Jung, Jongyun [1 ]
机构
[1] Ohio State Univ, Coll Med, Dept Biomed Informat, Columbus, OH 43210 USA
基金
美国国家卫生研究院;
关键词
Accuracy; Ensemble learning; Fracture; Genomics; Machine learning; Osteoporosis; Sensitivity; Specificity; Super Learner; OSTEOPOROTIC FRACTURES; MEN; RISK; PROBABILITY; WOMEN; MODEL;
D O I
10.1007/s00198-025-07437-w
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
This study presents an innovative ensemble machine learning model integrating genomic and clinical data to enhance the prediction of major osteoporotic fractures in older men. The Super Learner (SL) model achieved superior performance (AUC = 0.76, accuracy = 95.6%, sensitivity = 94.5%, specificity = 96.1%) compared to individual models. Ensemble machine learning improves fracture prediction accuracy, demonstrating the potential for personalized osteoporosis management. fracture risk models have limitations in their accuracy and in integrating genomic data. This study developed and validated an innovative ensemble machine learning (ML) model that combines multiple algorithms and integrates clinical, lifestyle, skeletal, and genomic data to enhance prediction for major osteoporotic fractures (MOF) in older men. Methods This study analyzed data from 5130 participants in the Osteoporotic Fractures in Men cohort Study. The model incorporated 1103 individual genome-wide significant variants and conventional risk factors of MOF. The participants were randomly divided into training (80%) and testing (20%) sets. Seven ML algorithms were combined using the SL ensemble method with tenfold cross-validation MOF prediction. Model performance was evaluated on the testing set using the area under the curve (AUC), the area under the precision-recall curve, calibration, accuracy, sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and reclassification metrics. SL model performances were evaluated by comparison with baseline models and subgroup analyses by race. Results The SL model demonstrated the best performance with an AUC of 0.76, accuracy of 95.6%, sensitivity of 94.5%, specificity of 96.1%, NPV of 95.1%, and PPV of 94.7%. Among the individual ML, gradient boosting performed optimally. The SL model outperformed baseline models, and it also achieved accuracies of 93.1% for Whites and 91.6% for Minorities, outperforming single ML in subgroup analysis. Conclusion The ensemble learning approach significantly improved fracture prediction accuracy and model performance compared to individual ML. Integrating genomic and phenotypic data via the SL approach represents a promising advancement for personalized osteoporosis management.
引用
收藏
页码:811 / 821
页数:11
相关论文
共 50 条
[41]   Improved prediction of soil liquefaction susceptibility using ensemble learning algorithms [J].
Tiwari, Satyam ;
Das, Sarat K. ;
Mohanty, Madhumita ;
Prakhar .
GEOMECHANICS AND ENGINEERING, 2024, 37 (05) :475-498
[42]   Accelerating the identification of the allergenic potential of plant proteins using a stacked ensemble-learning framework [J].
Charoenkwan, Phasit ;
Chumnanpuen, Pramote ;
Schaduangrat, Nalini ;
Shoombuatong, Watshara .
JOURNAL OF BIOMOLECULAR STRUCTURE & DYNAMICS, 2024,
[43]   A Novel Fracture Prediction Model Using Machine Learning in a Community-Based Cohort [J].
Kong, Sung Hye ;
Ahn, Daehwan ;
Kim, Buomsoo ;
Srinivasan, Karthik ;
Ram, Sudha ;
Kim, Hana ;
Hong, A. Ram ;
Kim, Jung Hee ;
Cho, Nam H. ;
Shin, Chan Soo .
JBMR PLUS, 2020, 4 (03)
[44]   SIMLIN: a bioinformatics tool for prediction of S-sulphenylation in the human proteome based on multi-stage ensemble-learning models [J].
Xiaochuan Wang ;
Chen Li ;
Fuyi Li ;
Varun S. Sharma ;
Jiangning Song ;
Geoffrey I. Webb .
BMC Bioinformatics, 20
[45]   A New Approach for Prediction of Solar Radiation with Using Ensemble Learning Algorithm [J].
Basaran, Kivanc ;
Ozcift, Akin ;
Kilinc, Deniz .
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING, 2019, 44 (08) :7159-7171
[46]   A New Approach for Prediction of Solar Radiation with Using Ensemble Learning Algorithm [J].
Kivanc Basaran ;
Akın Özçift ;
Deniz Kılınç .
Arabian Journal for Science and Engineering, 2019, 44 :7159-7171
[47]   Handling Imbalanced Data using Ensemble Learning in Software Defect Prediction [J].
Malhotra, Ruchika ;
Jain, Juhi .
PROCEEDINGS OF THE CONFLUENCE 2020: 10TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING, DATA SCIENCE & ENGINEERING, 2020, :300-304
[48]   An Ensemble Learning Approach for Accurate Energy Prediction in Residential Buildings [J].
Al-Rakhami, Mabrook ;
Gumaei, Abdu ;
Alsanad, Ahmed ;
Alamri, Atif ;
Hassan, Mohammad Mehedi .
IEEE ACCESS, 2019, 7 :48328-48338
[49]   Prediction of fracture toughness of concrete using the machine learning approach [J].
Shemirani, Alireza Bagher .
THEORETICAL AND APPLIED FRACTURE MECHANICS, 2024, 134
[50]   Data analytics in ensemble learning for effective crop yield prediction [J].
Tripathi, Deeksha ;
Biswas, Saroj K. ;
Baruah, Barnana .
ENGINEERING RESEARCH EXPRESS, 2024, 6 (03)