使用最小二乘支持向量回归和模糊聚类的混合方法早期诊断帕金森病

IF 5.3 2区 医学 Q1 ENGINEERING, BIOMEDICAL
Hossein Ahmadi , Lin Huo , Goli Arji , Abbas Sheikhtaheri , Shang-Ming Zhou
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引用次数: 0

摘要

帕金森病(PD)是一种影响大脑神经、行为和生理功能的神经退行性疾病,包括运动和非运动表现。虽然目前已有几种使用机器学习监督技术的帕金森病诊断系统,但要提高帕金森病早期检测的准确性,还需要做更多的努力。本文通过整合最小二乘支持向量回归(LS-SVR)和模糊聚类(Fuzzy Clustering),开发了一种用于帕金森病统一评分量表(UPDRS)诊断的新方法。本文使用特征选择和主成分分析(PCA)来克服数据中的多重共线性问题。本文使用了一个大型医疗数据集,包括 "运动-UPDRS "和 "总-UPDRS",通过广泛的评估和与现有方法的比较,展示了所提出的方法如何提高预测性能。与其他预测方法相比,实验结果表明,所提出的方法在总-UPDRS(均方根误差 = 0.7348;R2 = 0.9169)和运动-UPDRS(均方根误差 = 0.8321;R2 = 0.8756)预测方面提供了最佳准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Early diagnosis of Parkinson’s disease using a hybrid method of least squares support vector regression and fuzzy clustering

Parkinson’s disease (PD) is a neurodegenerative disorder that influence brain’s neurological, behavioral, and physiological functions and includes motor and nonmotor manifestations. Although there have been several PD diagnosis systems with supervised machine learning techniques, there are more efforts that need to enhance the accurate detection of PD in its early stage. The current paper developed a novel approach by integrating Least Squares Support Vector Regression (LS-SVR) and Fuzzy Clustering for Unified Parkinson’s Disease Rating Scale (UPDRS) diagnosis. This paper used feature selection and Principal Component Analysis (PCA) to overcome the multicollinearity issues in data. This paper used a large medical dataset including Motor- and Total-UPDRS to demonstrate how the proposed method can improve prediction performance via extensive evaluations and comparisons with existing methods. Compared to other prediction methods, the experimental results demonstrate that the proposed method provided the best accuracy for Total-UPDRS (Root Mean Squared Error = 0.7348; R2 = 0.9169) and Motor-UPDRS (Root Mean Squared Error = 0.8321; R2 = 0.8756) predictions.

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来源期刊
CiteScore
16.50
自引率
6.20%
发文量
77
审稿时长
38 days
期刊介绍: Biocybernetics and Biomedical Engineering is a quarterly journal, founded in 1981, devoted to publishing the results of original, innovative and creative research investigations in the field of Biocybernetics and biomedical engineering, which bridges mathematical, physical, chemical and engineering methods and technology to analyse physiological processes in living organisms as well as to develop methods, devices and systems used in biology and medicine, mainly in medical diagnosis, monitoring systems and therapy. The Journal''s mission is to advance scientific discovery into new or improved standards of care, and promotion a wide-ranging exchange between science and its application to humans.
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