An improved sex-specific and age-dependent classification model for Parkinson's diagnosis using handwriting measurement
An improved sex-specific and age-dependent classification model for Parkinson's diagnosis using handwriting measurement
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DOI：
10.1016/j.cmpb.2019.105305
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发表时间：
2020-06-01
影响因子：
6.1
通讯作者：
Joshi, Deepak
中科院分区：
文献类型：
--
作者：
Gupta, Ujjwal;Bansal, Hritik;Joshi, Deepak
背景和目标：以更高的准确性诊断帕金森氏症总是希望减缓疾病的进展并提高生活质量。有证据表明，男性和女性之间以及老年人和成年人之间存在固有的神经差异。然而，这种性别和年龄信息的潜力尚未被开发用于帕金森病的识别。方法：在本文中，我们开发了一种性别特异性和年龄依赖性的分类方法，使用帕金森病患者的在线手写记录来诊断帕金森病（n = 37; m/f-19/18;年龄-69.3 +/-10.9岁）和健康对照组（n = 38; m/f-20/18;年龄-62.4 +/-11.3岁）。一个支持向量机的排名方法是用来呈现特定的功能，他们的优势，在性别和年龄组的帕金森diagnosis. Results：性别特异性和年龄依赖的分类器，观察显着优于广义分类。与广义分类器的75.76%（SD = 1.17）准确率相比，女性特定分类器的准确率提高了83.75%（SD = 1.63），老年相关分类器的准确率提高了79.55%（SD = 1.58）。结论：结合年龄和性别信息在分类中被证明是令人鼓舞的。观察到一组不同的特征在不同类别的分类中占主导地位，以获得更高的分类精度。(C)2019 Elsevier B.V.版权所有。
Background and Objectives: Diagnosis of Parkinson's with higher accuracy is always desirable to slow down the progression of the disease and improved quality of life. There are evidences of inherent neurological differences between male and females as well as between elderly and adults. However, the potential of such gender and age infomration have not been exploited yet for Parkinson's identification.Methods: In this paper, we develop a sex-specific and age-dependent classification method to diagnose the Parkinson's disease using the online handwriting recorded from individuals with Parkinson's (n = 37; m/f-19/18;age-69.3 +/- 10.9yrs) and healthy controls (n = 38; m/f-20/18;age-62.4 +/- 11.3yrs). A support vector machine ranking method is used to present the features specific to their dominance in sex and age group for Parkinson's diagnosis.Results: The sex-specific and age-dependent classifier was observed significantly outperforming the generalized classifier. An improved accuracy of 83.75% (SD = 1.63) with the female-specific classifier, and 79.55% (SD = 1.58) with the old-age dependent classifier was observed in comparison to 75.76% (SD = 1.17) accuracy with the generalized classifier.Conclusions: Combining the age and sex information proved to be encouraging in classification. A distinct set of features were observed to be dominating for higher classification accuracy in a different category of classification. (C) 2019 Elsevier B.V. All rights reserved.