使用集成机器学习和稳定生物标记物的帕金森病高精度分类。

IF 3 Q2 CLINICAL NEUROLOGY
Ana Carolina Brisola Brizzi, Osmar Pinto Neto, Rodrigo Cunha de Mello Pedreiro, Lívia Helena Moreira
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引用次数: 0

摘要

背景:准确区分帕金森病(PD)与健康老龄化是及时干预和有效管理的关键。体位摇摆异常是PD的显著运动特征。定量稳定测定法和机器学习(ML)为开发支持诊断过程的客观标记提供了一条有前途的途径。本研究旨在开发和验证高性能ML模型,使用一套全面的稳定参数对PD患者和年龄匹配的健康老年人(HOAs)进行分类。方法:37例hoa(平均年龄70±6.8岁)和26例特发性PD (Hoehn和Yahr 2-3期,正在用药,平均年龄66±2.9岁),年龄均为60 ~ 80岁。利用力平台采集静立状态下睁眼和闭眼两种状态下的稳定性数据,提取反映压力中心(COP)摇摆时频域特征的34个参数。在数据预处理后,包括缺失值的平均值输入和特征缩放,三个ML分类器(随机森林,梯度增强和支持向量机)使用GridSearchCV进行超参数调整,并进行三重交叉验证。在此基础上构造了一个集成投票分类器(软投票)。使用15次分层训练-测试分割(70%训练和30%测试)和1000次迭代的额外bootstrap过程来严格评估模型性能,以获得可靠的95%置信区间(ci)。结果:我们优化的集成投票分类器具有出色的判别能力,将PD与hoa区分出来的平均准确率为0.91 (95% CI: 0.81-1.00),平均ROC曲线下面积(AUC ROC)为0.97 (95% CI: 0.92-1.00)。重要的是,特征分析显示,睁眼时的前后摇摆速度(V-AP)和闭眼时的总摇摆路径(TOD_EC,使用COP位移向量从其平均位置计算)是区分类群的最稳健和非侵入性的生物标志物。结论:利用稳定性特征的集成ML方法提供了一种高度准确、无创的方法来区分PD和健康老龄化,并可能增强临床评估和监测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
High-Accuracy Classification of Parkinson's Disease Using Ensemble Machine Learning and Stabilometric Biomarkers.

Background: Accurate differentiation of Parkinson's disease (PD) from healthy aging is crucial for timely intervention and effective management. Postural sway abnormalities are prominent motor features of PD. Quantitative stabilometry and machine learning (ML) offer a promising avenue for developing objective markers to support the diagnostic process. This study aimed to develop and validate high-performance ML models to classify individuals with PD and age-matched healthy older adults (HOAs) using a comprehensive set of stabilometric parameters. Methods: Thirty-seven HOAs (mean age 70 ± 6.8 years) and 26 individuals with idiopathic PD (Hoehn and Yahr stages 2-3, on medication; mean age 66 years ± 2.9 years), all aged 60-80 years, participated. Stabilometric data were collected using a force platform during quiet stance under eyes-open (EO) and eyes-closed (EC) conditions, from which 34 parameters reflecting the time- and frequency-domain characteristics of center-of-pressure (COP) sway were extracted. After data preprocessing, including mean imputation for missing values and feature scaling, three ML classifiers (Random Forest, Gradient Boosting, and Support Vector Machine) were hyperparameter-tuned using GridSearchCV with three-fold cross-validation. An ensemble voting classifier (soft voting) was constructed from these tuned models. Model performance was rigorously evaluated using 15 iterations of stratified train-test splits (70% train and 30% test) and an additional bootstrap procedure of 1000 iterations to derive reliable 95% confidence intervals (CIs). Results: Our optimized ensemble voting classifier achieved excellent discriminative power, distinguishing PD from HOAs with a mean accuracy of 0.91 (95% CI: 0.81-1.00) and a mean Area Under the ROC Curve (AUC ROC) of 0.97 (95% CI: 0.92-1.00). Importantly, feature analysis revealed that anteroposterior sway velocity with eyes open (V-AP) and total sway path with eyes closed (TOD_EC, calculated using COP displacement vectors from its mean position) are the most robust and non-invasive biomarkers for differentiating the groups. Conclusions: An ensemble ML approach leveraging stabilometric features provides a highly accurate, non-invasive method to distinguish PD from healthy aging and may augment clinical assessment and monitoring.

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来源期刊
Neurology International
Neurology International CLINICAL NEUROLOGY-
CiteScore
3.70
自引率
3.30%
发文量
69
审稿时长
11 weeks
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