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首页> 外文会议>Mediterranean Conference on Medical and Biological Engineering and Computing >Feasibility of Machine Learning in Predicting Features Related to Congenital Nystagmus
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Feasibility of Machine Learning in Predicting Features Related to Congenital Nystagmus

机译：预测先天性腹部震颤相关特征的机器学习的可行性

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摘要

Congenital nystagmus is an ocular-motor disease affecting people's visual acuity since their first years of life. Electrooculography is used to perform eye tracking in these patients, giving the possibility to extract a wide variety of parameters. The relationships among all these variables were analysed in the past and the aim of this paper is to perform a new analysis employing more recent techniques, those of machine learning. The electrooculography of 20 patients was recorded, signals were pre-processed, and some parameters were extracted through a custom-made software. Knime analytics platform was chosen in order to build predictive models using Random Forests and Logistic Regression Tree algorithms and some evaluation metrics were computed. The visual acuity and the variability of eye positioning were predicted employing five and six variables, respectively. In terms of coefficient of determination, visual acuity had values over 0.72 and variability of eye positioning over 0.70. Compared to the results obtained without machine learning algorithms during the past years, these values become more valuable. In conclusion, this approach showed its feasibility in detecting relationships among variables related to congenital nystagmus; it could be tested in order to find new and stronger relationships among these variables and be of support for clinicians.
机译：先天性眼囊肿是一种眼电机疾病，自他们的第一年以来影响人们的视力。电胶凝视用于在这些患者中进行眼睛跟踪，这促进了提取各种参数的可能性。过去分析了所有这些变量的关系，本文的目的是进行采用更新技术的新分析，机器学习。记录了20例患者的电胶胶，预处理信号，通过定制软件提取一些参数。选择了KNIME分析平台，以便使用随机林和逻辑回归树算法构建预测模型和一些评估度量。预测使用五个和六个变量的视力和眼睛定位的可变性。在测定系数方面，视敏度具有超过0.72的值，并且眼睛定位的可变性超过0.70。与在过去几年中没有机器学习算法的情况下获得的结果相比，这些值变得更加有价值。总之，这种方法在检测与先天性眼球震颤相关的变量之间的关系方面的可行性;可以测试它，以便在这些变量之间寻找新的和更强的关系，并对临床医生提供支持。

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