@inproceedings{e0f5e1e4f4b94717aa0e6e05c1087f1c,
title = "Prediction of Parkinson disease using gait signals",
abstract = "In the medical field, a number of data-mining methods and algorithms have been applied to help the decision-making process extract meaningful information from medical data. The goal of data mining is to establish efficient analysis and capture the hidden features of medical data. This paper is aims to find out the efficient model to identified the Parkinson disease people. Some experiments will be run to classify healthy people from those with Parkinson's disease. Data are recorded for 14 patients and 15 healthy individuals. A comparative study of the performance of multilayer neural network, support vector machine and decisiontree classifiers.The features derived from temporal domain and frequency domain will be used to train each classifier. The performances of the classifiers are evaluated using five metrics: classification accuracy, sensitivity, specificity and area under the receiver operating characteristic curve. The best classification accuracy achieved by multi layer neural network is 91.18\% using the extracted features and clinical information.",
keywords = "Data mining, Decisiontree Introduction, Feature extraction, Gait signals, Multilayer neural network, support vector machine",
author = "Haya Alaskar and Abir Hussain",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 11th International Conference on Developments in eSystems Engineering, DeSE 2018 ; Conference date: 02-09-2018 Through 05-09-2018",
year = "2018",
month = jul,
day = "2",
doi = "10.1109/DeSE.2018.00011",
language = "English",
series = "Proceedings - International Conference on Developments in eSystems Engineering, DeSE",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "23--26",
booktitle = "Proceedings - 11th International Conference on Developments in eSystems Engineering, DeSE 2018",
address = "United States",
}