(Similarity) Implementation of xgboost for classification of parkinson’s disease

Ginanjar, Abdurrahman (2019) (Similarity) Implementation of xgboost for classification of parkinson’s disease. ICCGANT.

Text 2.1Implementation of xgboost for classification of parkinson;s disease.pdf Download (8MB)

Abstract

Parkinson's Disease (PD) is an advanced neurodegenerative illness. It is about 90%
of PD sufferer shows speech disorders in the initial stages. Hence, in this research, speech
features were applied to classify this illness. The most famous speech features used in PD
research are jitter, shimmer, fundamental frequency parameters, harmonicity parameters,
Recurrence Period Density Entropy (RPDE), Detrended Fluctuation Analysis (DFA), and Pitch
Period Entropy (PPE).Those features were then called as baseline features used in this
research. In this research, the XGBoost algorithm was used for the classification of PD.
Initially, the whole baseline features were used in the XGBoost algorithm and obtained an
accuracy score of the model 84.80%. For improving the model, feature selection was
performed by plotting feature importance, which causes features of locShimmer (Fscore = 3)
was excluded from the model. After feature selection was performed, the accuracy score of the
model has increased to 85.60 %. We tried to improve the model using for second features
selection, by excluding features with F-score values less than 20. However, after performed
this feature selection, the accuracy of the model was decreased to 84.40 %. Thus, the model
used is the model with an accuracy of 85.60%.

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Item Type:	Peer Review
Subjects:	000 Computer Science, Information, & General Works > 004 Data Processing, Computer Science
Divisions:	Faculty of Engineering > Department of Informatics Engineering (S1)
Depositing User:	GINANJAR ABDURRAHMAN | abdurrahmanginanjar@unmuhjember.ac.id
Date Deposited:	15 Mar 2023 01:54
Last Modified:	15 Mar 2023 01:54
URI:	http://repository.unmuhjember.ac.id/id/eprint/16580

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