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Development of Intelligent Parkinson Disease Detection System Based on Machine Learning Techniques Using Speech Signal

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@article{IJASEIT12202,
   author = {Mohammed Younis Thanoun and Mohammad Tariq Yaseen and A.M. Aleesa},
   title = {Development of Intelligent Parkinson Disease Detection System Based on Machine Learning Techniques Using Speech Signal},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {11},
   number = {1},
   year = {2021},
   pages = {388--392},
   keywords = {Parkinson's disease; machine learning; PD, SMOTE; extra tree classifier.},
   abstract = {

Parkinson's disease is a brain condition that induces difficulty walking, standing, concentrating, trembling, and weakness. Parkinson's symptoms typically begin slowly and increase with time. Whenever the condition develops, individuals can experience trouble walking and communicating to others. Old people mostly tend to suffer from this disease and the number is expected to increase in the future. Machine learning (ML) techniques could help in the medical field in processing and analyzing data that offer good solutions in this field in terms of high accuracy and less required time compared to conventional methods. In this study, we proposed an enhanced methodology based on utilizing SMOTE to balance the dataset, due to the available dataset is imbalanced. then adopted extra tree classifier with k-fold technique after we balanced the dataset with SMOTE. we have achieved the best accuracy with respect to the classification accuracy in the literature, the obtained accuracy of our proposed model was higher than the used approaches in the related works. The new model for classifying the Parkinson's disease-dataset with class-imbalance data distribution achieved an accuracy of 96.52% by using our proposed method. The result shown that the dataset is lacked of balancing and it proves that the balancing in the dataset is important specially in medical classification. The impact of Optimal function selection, either automated by PCA or manually carried out, is clearly still being studied, and plays an essential role in improving the performance of machine learning.

},    issn = {2088-5334},    publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},    url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=12202},    doi = {10.18517/ijaseit.11.1.12202} }

EndNote

%A Thanoun, Mohammed Younis
%A Yaseen, Mohammad Tariq
%A Aleesa, A.M.
%D 2021
%T Development of Intelligent Parkinson Disease Detection System Based on Machine Learning Techniques Using Speech Signal
%B 2021
%9 Parkinson's disease; machine learning; PD, SMOTE; extra tree classifier.
%! Development of Intelligent Parkinson Disease Detection System Based on Machine Learning Techniques Using Speech Signal
%K Parkinson's disease; machine learning; PD, SMOTE; extra tree classifier.
%X 

Parkinson's disease is a brain condition that induces difficulty walking, standing, concentrating, trembling, and weakness. Parkinson's symptoms typically begin slowly and increase with time. Whenever the condition develops, individuals can experience trouble walking and communicating to others. Old people mostly tend to suffer from this disease and the number is expected to increase in the future. Machine learning (ML) techniques could help in the medical field in processing and analyzing data that offer good solutions in this field in terms of high accuracy and less required time compared to conventional methods. In this study, we proposed an enhanced methodology based on utilizing SMOTE to balance the dataset, due to the available dataset is imbalanced. then adopted extra tree classifier with k-fold technique after we balanced the dataset with SMOTE. we have achieved the best accuracy with respect to the classification accuracy in the literature, the obtained accuracy of our proposed model was higher than the used approaches in the related works. The new model for classifying the Parkinson's disease-dataset with class-imbalance data distribution achieved an accuracy of 96.52% by using our proposed method. The result shown that the dataset is lacked of balancing and it proves that the balancing in the dataset is important specially in medical classification. The impact of Optimal function selection, either automated by PCA or manually carried out, is clearly still being studied, and plays an essential role in improving the performance of machine learning.

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=12202 %R doi:10.18517/ijaseit.11.1.12202 %J International Journal on Advanced Science, Engineering and Information Technology %V 11 %N 1 %@ 2088-5334

IEEE

Mohammed Younis Thanoun,Mohammad Tariq Yaseen and A.M. Aleesa,"Development of Intelligent Parkinson Disease Detection System Based on Machine Learning Techniques Using Speech Signal," International Journal on Advanced Science, Engineering and Information Technology, vol. 11, no. 1, pp. 388-392, 2021. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.11.1.12202.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Thanoun, Mohammed Younis
AU  - Yaseen, Mohammad Tariq
AU  - Aleesa, A.M.
PY  - 2021
TI  - Development of Intelligent Parkinson Disease Detection System Based on Machine Learning Techniques Using Speech Signal
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 11 (2021) No. 1
Y2  - 2021
SP  - 388
EP  - 392
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Parkinson's disease; machine learning; PD, SMOTE; extra tree classifier.
N2  - 

Parkinson's disease is a brain condition that induces difficulty walking, standing, concentrating, trembling, and weakness. Parkinson's symptoms typically begin slowly and increase with time. Whenever the condition develops, individuals can experience trouble walking and communicating to others. Old people mostly tend to suffer from this disease and the number is expected to increase in the future. Machine learning (ML) techniques could help in the medical field in processing and analyzing data that offer good solutions in this field in terms of high accuracy and less required time compared to conventional methods. In this study, we proposed an enhanced methodology based on utilizing SMOTE to balance the dataset, due to the available dataset is imbalanced. then adopted extra tree classifier with k-fold technique after we balanced the dataset with SMOTE. we have achieved the best accuracy with respect to the classification accuracy in the literature, the obtained accuracy of our proposed model was higher than the used approaches in the related works. The new model for classifying the Parkinson's disease-dataset with class-imbalance data distribution achieved an accuracy of 96.52% by using our proposed method. The result shown that the dataset is lacked of balancing and it proves that the balancing in the dataset is important specially in medical classification. The impact of Optimal function selection, either automated by PCA or manually carried out, is clearly still being studied, and plays an essential role in improving the performance of machine learning.

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=12202 DO - 10.18517/ijaseit.11.1.12202

RefWorks

RT Journal Article
ID 12202
A1 Thanoun, Mohammed Younis
A1 Yaseen, Mohammad Tariq
A1 Aleesa, A.M.
T1 Development of Intelligent Parkinson Disease Detection System Based on Machine Learning Techniques Using Speech Signal
JF International Journal on Advanced Science, Engineering and Information Technology
VO 11
IS 1
YR 2021
SP 388
OP 392
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Parkinson's disease; machine learning; PD, SMOTE; extra tree classifier.
AB 

Parkinson's disease is a brain condition that induces difficulty walking, standing, concentrating, trembling, and weakness. Parkinson's symptoms typically begin slowly and increase with time. Whenever the condition develops, individuals can experience trouble walking and communicating to others. Old people mostly tend to suffer from this disease and the number is expected to increase in the future. Machine learning (ML) techniques could help in the medical field in processing and analyzing data that offer good solutions in this field in terms of high accuracy and less required time compared to conventional methods. In this study, we proposed an enhanced methodology based on utilizing SMOTE to balance the dataset, due to the available dataset is imbalanced. then adopted extra tree classifier with k-fold technique after we balanced the dataset with SMOTE. we have achieved the best accuracy with respect to the classification accuracy in the literature, the obtained accuracy of our proposed model was higher than the used approaches in the related works. The new model for classifying the Parkinson's disease-dataset with class-imbalance data distribution achieved an accuracy of 96.52% by using our proposed method. The result shown that the dataset is lacked of balancing and it proves that the balancing in the dataset is important specially in medical classification. The impact of Optimal function selection, either automated by PCA or manually carried out, is clearly still being studied, and plays an essential role in improving the performance of machine learning.

LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=12202 DO - 10.18517/ijaseit.11.1.12202