发表机构
COMSATS University Islamabad; National Center for Physics（伊斯兰堡COMSATS大学; 国家物理中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究整合三轴IMU传感器与集成学习，采用多种机器学习模型对比分析，发现LightGBM模型对帕金森病严重程度分类效能最优，达约97%。
AI 中文摘要
帕金森病（PD）是一种进行性神经退行性疾病，会对运动功能产生显著影响，导致出现震颤、强直、姿势不稳和运动迟缓等症状。及时的临床治疗、疾病管理和患者生活质量与早期且恰当的PD识别密切相关。近年来，可穿戴传感器技术与人工智能（AI）的发展，使得创建非侵入式、数据驱动的疾病检测方法成为可能。本文提出了一种基于人工智能的对比系统，通过分析惯性测量单元（IMU）采集的运动与震颤数据来检测帕金森病，该数据包含加速度计和陀螺仪传感器在X、Y、Z三个方向上的运动信号，这些症状为与PD相关的细微运动缺陷提供了有用信息。研究使用了多种分类模型来对比其有效性，包括支持向量机（SVM）、逻辑回归（LR）、K近邻（KNN）、决策树（DT）、极端梯度提升（XGBoost）和轻量级梯度提升机（LightGBM）。其中，逻辑回归模型在所有评估指标上的性能约为75%，K近邻（KNN）约为90%；支持向量机（SVM）的性能接近94%，决策树和XGBoost等分类器的整体分类效能接近96%；LightGBM模型在所有评估方法中始终保持最佳排名，其准确率、精确率、召回率和F1分数约为97%。结果表明，所提出的机器学习方法在PD严重程度分类中提供了准确且有效的预测能力。
英文摘要
Parkinson disease PD is a progressive neurodegenerative disease that can have a significant impact on motor performance resulting in the appearance of symptoms such as tremors rigidity postural instabilities and bradykinesia. Timely clinical treatment disease management and quality life of the patients are closely linked to early and appropriate identification of PD. Over the past few years the growth of wearable sensor technology and artificial intelligence AI have made it possible to create noninvasive and data driven disease detection methods. This paper proposes a comparative system using artificial intelligence to detect Parkinsons disease by analyzing the motion and tremor data captured by an inertial measurement unit IMU. The data comprises the signals of the acceleration and gyroscope sensors measuring movement in three directions X Y and Z. The signs and symptoms provide helpful information about subtle motor deficits associated with PD. Several classification models like Support Vector Machine SVM Logistic Regression LR KNearest Neighbors KNN Decision Tree DT Extreme Gradient Boosting XGBoost and Light Gradient Boosting Machine LightGBM were used to compare their effectiveness. The Logistic Regression model had a performance around 75 percent in all evaluation metrics and KNearest Neighbours KNN around 90 percent. The support vector machine SVM performed almost 94 percent whereas the performance of classifiers such as Decision Tree and XGBoost was close to 96 percent and overall classification efficacy respectively. LightGBM model performs consistently at the best rank among all of the evaluated methods having Accuracy, Precision, Recall and F1score of around 97 percent. The results show that the proposed machine learning approach offers an accurate and effective predictive capability in the classification of PD severity.
