A Comparative Study of Early Detection of Parkinson's Disease using Machine Learning Techniques

Shail Raval, Rahil Balar, Vibha Patel
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引用次数: 10

Abstract

Parkinson's Disease (PD) is considered a malison for mankind for several decades. Its detection with the help of an automated system is a subject undergoing intense study. This entails a need for incorporating a machine learning model for the early detection of PD. For discovering a full proof model, the cardinal prerequisite is to study the existing computational intelligent techniques in the field of research used for PD detection. Many existing models focus on singular modality or have a cursory analysis of multiple modalities. This encouraged us to provide a comparative literature study of four main modalities signifying major symptoms used for early detection of PD, namely, tremor at rest, bradykinesia, rigidity, and, voice impairment. State- of-the-art machine learning implementations namely Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), K-nearest neighbors (KNN), Stochastic Gradient Descent (SGD) and Gaussian Naive Bayes (GNB) are executed in these modalities with their respective datasets. Furthermore, ensemble approaches such as Random Forest Classifier (RF), Adaptive Boosting (AB) and Hard Voting (HV) are implemented. Our results are compared with those obtained with their respective researches. Among all the tests, applying Random Forest (RF) on Static Spiral Test (for detecting tremor) gave us the most significant result, i.e. the highest accuracy of 99.79%. This leads to the conclusion that the multimodal approach with the help of the ensemble method should be used to get better and accurate results.
使用机器学习技术早期检测帕金森病的比较研究
帕金森病(PD)被认为是人类几十年来的顽疾。在自动化系统的帮助下对其进行检测是一个正在深入研究的课题。这就需要将机器学习模型用于PD的早期检测。为了发现一个完整的证明模型,最重要的前提是研究现有的用于PD检测的计算智能技术。许多现有的模型都侧重于单一模态或对多模态进行粗略的分析。这鼓励我们对早期发现PD的主要症状的四种主要模式进行比较文献研究,即静止时震颤、运动迟缓、僵硬和声音障碍。最先进的机器学习实现,即逻辑回归(LR),支持向量机(SVM),决策树(DT), k近邻(KNN),随机梯度下降(SGD)和高斯朴素贝叶斯(GNB)在这些模式下使用各自的数据集执行。此外,还实现了随机森林分类器(RF)、自适应增强(AB)和硬投票(HV)等集成方法。我们的研究结果与他们各自的研究结果进行了比较。在所有测试中,将随机森林(RF)应用于静态螺旋测试(用于检测震颤)给了我们最显著的结果,即99.79%的最高准确率。由此得出结论,为了得到更好、更准确的结果,应采用集成方法辅助的多模态方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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