Open AccessFeature Selection Techniques to Choose the Best Features for Parkinsons Disease Predictions Based on Decision Tree
Author(s) -
. Yulianti,
A. N. Syapariyah,
Aries Saifudin,
Teti Desyani
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1477/3/032008
Subject(s) - feature selection , feature (linguistics) , computer science , disease , machine learning , decision tree , artificial intelligence , selection (genetic algorithm) , decision tree model , tree (set theory) , parkinson's disease , pattern recognition (psychology) , medicine , mathematics , philosophy , linguistics , pathology , mathematical analysis
Parkinson is a disease that is caused by nerve cell damage in the brain and incurable. Knowing about Parkinson disease is very important so that medical action can be taken to prevent Parkinson’s getting worse. The dataset that uses to analysis for Parkinson disease using machine learning algorithms has many features. The dataset with many features can increase complexity, but not all features have a positive influence on the results of the analysis. Irrelevant features can reduce model performance. This research proposes to apply feature selection to choose features that have a positive effect so that the performance of the model does not decrease. The experiment results show that the application of feature selection can lead to better model performance.