Department of Computational Science, Trakya University, Edirne, Turkey.
Department of Electrical and Electronics Engineering, Trakya University, Edirne, Turkey.
Comput Methods Biomech Biomed Engin. 2023 Oct;26(13):1653-1667. doi: 10.1080/10255842.2023.2245516. Epub 2023 Aug 21.
Parkinson's disease (PD) is one of the most widespread neurological disorders associated with nerve damage without definitive treatment. Impairments, such as trembling and slowing down in hand movements are among the first symptoms. For this purpose, in this study, machine learning (ML)-based models were developed by using keyboard keystroke dynamics. According to patients' drug use status, disease severity, and gender, we created 14 different sub-datasets and extracted 378 features using raw keystroke data. We developed alternative models with Support Vector Machines (SVM), k-Nearest Neighbors (kNN), and Random Forest (RF) algorithms. We further used Minimum Redundancy Maximum Relevance (mRmR), RELIEF, sequential forward selection (SFS), and RF feature selection methods to investigate prominent features in distinguishing PD. We developed ML models that jointly use the most popular features of selection algorithms (feature ensemble [FE]) and an ensemble classifier by combining multiple ML algorithms utilizing majority vote (model ensemble [ME]). With 14 different sets, FE and ME models provided accuracy (Acc.) in the range of 91.73 - 100% and 81.08 - 100%, respectively.
帕金森病（PD）是最常见的神经退行性疾病之一，与无明确治疗方法的神经损伤有关。震颤和手部运动减慢等障碍是最早出现的症状之一。为此，在这项研究中，我们使用基于机器学习（ML）的模型，通过键盘按键动力学来开发这些模型。根据患者的药物使用情况、疾病严重程度和性别，我们创建了 14 个不同的子数据集，并使用原始按键数据提取了 378 个特征。我们使用支持向量机（SVM）、k-最近邻（kNN）和随机森林（RF）算法开发了替代模型。我们进一步使用最小冗余最大相关性（mRmR）、RELIEF、顺序前向选择（SFS）和 RF 特征选择方法来研究区分 PD 的显著特征。我们开发了 ML 模型，这些模型共同使用了选择算法的最受欢迎特征（特征集成[FE]）和一个集成分类器，该分类器通过结合多个 ML 算法并利用多数票（模型集成[ME]）来实现。对于 14 个不同的数据集，FE 和 ME 模型提供的准确率（Acc.）分别在 91.73%至 100%和 81.08%至 100%的范围内。
