基于多层次集成学习的骨质疏松辅助诊断研究

目的 原发性骨质疏松是一种起病隐匿、病程较长，在中老年人中高发的疾病，其可引起包括骨折在内的一系列严重症状，是我国中老年人致残致死的主要原因之一。与骨质疏松相关的生理检验指标有很多，如何筛选利用这些指标为诊断服务、建立诊断模型，尚未有成熟、统一的方法。方法 利用人工智能相关技术，对临床骨质疏松患者指标使用多种特征相关性算法进行特征选择，并在此基础上提出了一种多层次的集成学习框架：SAB-SVMKNN算法，其通过将内部同质学习器集成和外部异质学习器集成结合，将集成学习中的Boosting算法和Bagging算法使用Stacking进行集成，构建性能更强，适应性更好地诊断预测模型。结果 使用特征选择从原始数据中的31项临床指标中筛选了对于骨质疏松最重要的8种相关特征，通过这种方式使各模型准确率平均提高了9.2%,且该研究对应的模型准确率提升18.6%,最终达到了94.8%的准确率。结论 特征选择对于临床诊断和骨质疏松疾病的研究具有重要意义，该研究构建的预测模型可以有助于提高医生的诊断准确率。

Research on the assistant diagnosis of osteoporosis based on multi-level ensemble learning

Objective Osteoporosis, a skeletal disease with a high incidence, insidious onset, and long course, which can cause a series of severe symptoms including fractures, is one of the leading causes of disability and mortality among the elderly in our country. There are many physiological test indexes related to osteoporosis. However, there is no mature and unified method to screen and to diagnosis at present. Methods Artificial intelligence technology was used to select features from indicators of clinical osteoporosis patients using a variety of feature-related algorithms. Based on this, a multi-level ensemble learning framework was proposed, including SAB-SVMKNN algorithm, which combined internal homogeneous learning with external heterogeneous learning. Boosting and Bagging algorithms were integrated in learning with Stacking to build a diagnostic prediction model with better performance and adaptability. Results Eight of the most important features for osteoporosis were selected from 31 clinical indicators in the original data by feature selection algorithm, which improved the accuracy of each model by an average of 9.2%, and the corresponding model accuracy in this study increased by 18.6%, reaching a final accuracy of 94.8%. Conclusion Feature selection is of great significance for clinical diagnosis and the study of osteoporotic diseases. The constructed prediction model can improve the diagnosis accuracy for physicians.