Evaluation of Feature Selection Techniques for Predicting Parkinson's Disease using Machine Learning Models
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- @InProceedings{Akilandeswari:2025:ICEARS,
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author = "A. Akilandeswari",
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title = "Evaluation of Feature Selection Techniques for
Predicting {Parkinson's} Disease using Machine Learning
Models",
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booktitle = "2025 International Conference on Electronics and
Renewable Systems (ICEARS)",
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year = "2025",
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pages = "1431--1435",
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month = feb,
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keywords = "genetic algorithms, genetic programming, Logistic
regression, Renewable energy sources, Accuracy,
Parkinson's disease, Pipelines, Machine learning,
Predictive models, Feature extraction, Random forests,
Optimisation, Parkinson's Disease Prediction, Random
Forest, Boruta, Recursive Feature Elimination (RFE),
TPOT",
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DOI = "
doi:10.1109/ICEARS64219.2025.10940164",
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abstract = "Parkinson's disease is one of the major progressive
neurodegenerative disorders that majorly affect both
motor and non-motor functions. The significance of this
study lies in spotlighting the importance of early,
accurate detection of Parkinson's disease in the
betterment of patient outcomes as well as management.
This paper sets out to make a case discussion about the
use of machine learning models in predictive
Parkinson's disease through feature selection. We apply
the following methods to machine learning models,
including Random Forest and Logistic Regression, in
order to compare approaches, namely Boruta, Recursive
Feature Elimination with Cross-Validation (RFECV),
TPOT-Tree-based Pipeline Optimisation Tool, and
Variance Threshold. The Random Forest model with
Variance Threshold feature selection showed a high
accuracy of 95percent. Our experiments also present the
fact that feature selection is relevant to improve the
performance of the model: indeed, different techniques
show different levels of prediction accuracy.
ConclusionThis paper has proven it possible to improve
the prediction of disease to a considerable extent by
the right selection of the feature selection technique
and machine learning model.",
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notes = "Also known as \cite{10940164}",
- }
Genetic Programming entries for
A Akilandeswari
Citations