An Intelligent Computing Based Approach for Parkinson Disease Detection

Ashish Ranjan, A. Swetapadma
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引用次数: 2

Abstract

In this work various machine learning techniques such as support vector machine (SVM), nearest neighbor (k-NN), artificial neural network (ANN) has been used for detection of Parkinson disease. Input features are collected from the handwriting of various normal and Parkinson persons. The inputs and their corresponding targets are given to machine learning based methods. A comparative study of SVM, k-NN and ANN has been carried out. Accuracy of the proposed method is found to be 100% for all the tested data. Hence machine learning based methods can be put to use in real time scenario.
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一种基于智能计算的帕金森病检测方法
在这项工作中,各种机器学习技术,如支持向量机(SVM)、最近邻(k-NN)、人工神经网络(ANN)已被用于帕金森病的检测。输入特征从各种正常人和帕金森患者的笔迹中收集。输入和它们对应的目标被给予基于机器学习的方法。对支持向量机、k-NN和人工神经网络进行了比较研究。对于所有测试数据,所提出的方法的准确性为100%。因此,基于机器学习的方法可以用于实时场景。
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