@inproceedings{af6f7dd0261547e1916372cfdcef68c7,
title = "Early diagnosis of Parkinson's disease via machine learning on speech data",
abstract = "Using two distinct data sets (from the USA and Germany) of healthy controls and patients with early or mild stages of Parkinson's disease, we show that machine learning tools can be used for the early diagnosis of Parkinson's disease from speech data. This could potentially be applicable before physical symptoms appear. In addition, we show that while the training phase of machine learning process from one country can be reused in the other; different features dominate in each country; presumably because of languages differences. Three results are presented: (i) separate training and testing by each country (close to 85\% range); (ii) pooled training and testing (about 80\% range) and (iii) cross-country (training in one and testing in the other) (about 75\% ranges). We discovered that different feature sets were needed for each country (language).",
keywords = "Classification, Early Diagnosis, Machine Learning, Parkinson Disease, Pattern Matching, SVM, Speech Data",
author = "Hananel Hazan and Dan Hilu and Larry Manevitz and Ramig, \{Lorraine O.\} and Shimon Sapir",
year = "2012",
doi = "10.1109/EEEI.2012.6377065",
language = "אנגלית",
isbn = "9781467346801",
series = "2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012",
booktitle = "2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012",
note = "2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012 ; Conference date: 14-11-2012 Through 17-11-2012",
}