Early Screening of DDH using SVM Classification
Early Screening of DDH using SVM Classification
复制标题
使用 SVM 分类早期筛查 DDH
DOI：
10.1109/southeastcon42311.2019.9020565
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Kishan Neupane
中科院分区：
文献类型：
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作者：
MD Erfanul Alam;N. Smith;Daren Watson;T. Hassan;Kishan Neupane
髋关节发育不良（DDH）的治疗变得不那么复杂，如果它是早期发现的。本文利用声学非侵入性数据对DDH进行检测。我们研究早期检测DDH使用机器学习技术，通过支持向量机（SVM）技术。我们使用的数据从一个建议的方法，测试不同的简化模型的髋关节。用带限白色声学噪声（10-2500 Hz）刺激模型，并测量模型的响应。我们获得相位，传递函数和相干性作为不同的模拟髋关节发育不良水平和模拟正常情况下的功能。结果表明，线性支持向量机给出了79%的整体准确性为4类曲线下面积（AUC）为0.93的最脱位的髋关节在受试者工作特征（ROC）曲线。
Treatment of Developmental Dysplasia of Hip (DDH) becomes less convoluted if it is detected early. In this paper, an acoustic non-invasive data is used for detection of DDH. We investigate early detection of DDH using machine learning technique through support vector machine (SVM) technique. We use data from a proposed method that tested different simplified models of the hip joint. Models were stimulated with band-limited white acoustic noise (10-2500 Hz) and the response of the model was measured. We obtain phase, transfer function and coherence as features for different simulated hip dysplasia levels and for simulated normal cases. Results shows that linear SVM gives an overall accuracy of 79% for 4 class with an area under the curve (AUC) of.93 for the most dislocated hip joint in receiver operating characteristic (ROC) curve.