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Features selection for building an early diagnosis machine learning model for Parkinson's disease

机译：为帕金森病的早期诊断机学习模型构建特点

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In this work, different approaches were evaluated to optimize building machine learning classification models for the early diagnosis of the Parkinson disease. The goal was to sort the medical measurements and select the most relevant parameters to build a faster and more accurate model using feature selection techniques. Decreasing the number of features to build a model could lead to more efficient machine learning algorithm and help doctors to focus on what are the most important measurements to take into account. For feature selection we compared the Filter and Wrapper techniques. Then we selected a good machine learning algorithm to detect which technique could help us by calculate the crossover scores for each technique. This research is based on a dataset which was created by Athanasius Tsanas and Max Little of the University of Oxford, in collaboration with 10 medical centers in the US and Intel Corporation. This target of these medical measurements is to find the Unified Parkinson's disease rating scale (UPDRS) which is the most commonly used scale for clinical studies of Parkinson's disease.
机译：在这项工作中，评估了不同的方法，以优化建筑机器学习分类模型，以便于帕金森病的早期诊断。目标是对医疗测量进行排序，并选择最相关的参数，使用特征选择技术构建更快更准确的模型。减少构建模型的功能数量可能导致更有效的机器学习算法，并帮助医生专注于要考虑的最重要的测量值。对于特征选择，我们比较了过滤器和包装器技术。然后我们选择了一个好的机器学习算法来检测哪种技术可以通过计算每种技术的交叉分数来帮助我们。本研究基于由Athanasius Tsanas和Max Little的Athanasius Tsanas和Max Little，与美国和英特尔公司的10个医疗中心合作创建的数据集。这些医学测量的目标是找到统一的帕金森病评级规模（UPDRS），这是帕金森病的临床研究中最常用的规模。

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