Research on Osteoporosis Risk Assessment Based on Semi-supervised Machine Learning
首发时间:2020-05-19
Abstract:This paper proposes a semi-supervised machine learning method for osteoporosis risk assessment. Existing osteoporosis risk assessment models have problems of low accuracy, and cannot utilize large amounts of unlabeled data. In order to improve the accuracy of diagnosis, the method comprehensively considers the osteoporosis-related questionnaire data and bone image data, and fuses the multi-modal features extracted from them. Feature engineering and Word2vec are used to extract numerical and text features from questionnaires, respectively. CNN is used to extract image features from BMD images. Considering the difficulty of obtaining labeled medical data, this paper builds a self-training semi-supervised model based on XGBoost to classify and evaluate osteoporosis, which uses both labeled and unlabeled data for obtaining better generalization capabilities. Besides, in view of the fact that the questionnaire data has plenty of outliers and missing data, this paper removes outliers based on a DBSCAN algorithm and propose an improved PKNN algorithm to impute the missing data. Experimental results show that the proposed improved semi-supervised method achieves an accuracy of 0.78 in osteoporosis risk assessment and has obvious advantages compared with other methods.
keywords: Machine Learning;Osteoporosis Semi-supervised Feature fusion
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基于半监督机器学习的骨质疏松症风险评估研究
摘要:本文提出了一种基于半监督机器学习的骨质疏松症风险评估方法。现有的骨质疏松症风险评估模型存在准确率低的问题,且无法利用大量未标记的数据。为了提高诊断的准确率,该方法综合考虑了与骨质疏松症相关的问卷数据和骨骼图像数据,分别通过特征工程和Word2vec从问卷中提取数值特征和文本特征,利用CNN从骨密度图像中提取图像特征,并进行多模态特征融合。考虑到有标记数据难以获取,本文构建了基于XGBoost的自训练半监督模型对骨质疏松症进行分类和评估,同时利用标记和未标记数据,以获得更好的泛化能力。此外,针对问卷数据存在大量异常值和缺失数据的情况,本文使用DBSCAN算法去除异常值,并提出一种改进的PKNN算法对缺失数据进行填充。实验结果表明,改进的半监督方法在骨质疏松症风险评估中达到了0.78 的准确率,与其他方法相比具有明显优势。
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基于半监督机器学习的骨质疏松症风险评估研究
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