Developing and comparing deep learning and machine learning algorithms for osteoporosis risk prediction

作者：Chuan Qiu, Kuan‐Jui Su, Zhe Luo, Qing Tian, Lan‐Juan Zhao, Li Wu, Hong‐Wen Deng, Hui Shen · 发表于：Frontiers in Artificial Intelligence · 年份：2024 · DOI：10.3389/frai.2024.1355287 · 被引用次数：21 · 研究领域：Bone health and osteoporosis research、Artificial Intelligence in Healthcare、Artificial Intelligence in Healthcare and Education

Introduction: Osteoporosis, characterized by low bone mineral density (BMD), is an increasingly serious public health issue. So far, several traditional regression models and machine learning (ML) algorithms have been proposed for predicting osteoporosis risk. However, these models have shown relatively low accuracy in clinical implementation. Recently proposed deep learning (DL) approaches, such as deep neural network (DNN), which can discover knowledge from complex hidden interactions, offer a new opportunity to improve predictive performance. In this study, we aimed to assess whether DNN can achieve a better performance in osteoporosis risk prediction. Methods: By utilizing hip BMD and extensive demographic and routine clinical data of 8,134 subjects with age more than 40 from the Louisiana Osteoporosis Study (LOS), we developed and constructed a novel DNN framework for predicting osteoporosis risk and compared its performance in osteoporosis risk prediction with four conventional ML models, namely random forest (RF), artificial neural network (ANN), k-nearest neighbor (KNN), and support vector machine (SVM), as well as a traditional regression model termed osteoporosis self-assessment tool (OST). Model performance was assessed by area under 'receiver operating curve' (AUC) and accuracy. Results: By using 16 discriminative variables, we observed that the DNN approach achieved the best predictive performance (AUC = 0.848) in classifying osteoporosis (hip BMD T-score ≤ -1.0)...