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This research aims to enhance predictive modeling for Parkinson's disease using a fuzzy neural network and feature selection techniques.

Enhanced Parkinson's disease prediction using LDEFS feature selection and Mamdani fuzzy neural network

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Model demonstrates 95.8% prediction accuracy in early Parkinson's diagnosis, highlighting the value of feature selection and fuzzy neural modeling.

This research aims to enhance predictive modeling for Parkinson's disease using a fuzzy neural network and feature selection techniques.
Dataset obtained from an online repository
Z-Score Normalization applied for data quality
Disease Affect Scaling Rate used for feature ranking
Logistic Decision Exhaustive Feature Selection to identify key features
Mamdani Fuzzy Neural Network model developed for predictions.
Achieved prediction accuracy of 95.8% for early PD detection
F-measure of 95.3%, outperforming standard machine-learning classifiers
Integration of exercise-related patterns improved classification robustness.

Vijayalakshmi et al. (2025) studied this question.

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View Full Paper

Ask AI

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Authors

Model demonstrates 95.8% prediction accuracy in early Parkinson's diagnosis, highlighting the value of feature selection and fuzzy neural modeling.

This research aims to enhance predictive modeling for Parkinson's disease using a fuzzy neural network and feature selection techniques.
Dataset obtained from an online repository
Z-Score Normalization applied for data quality
Disease Affect Scaling Rate used for feature ranking
Logistic Decision Exhaustive Feature Selection to identify key features
Mamdani Fuzzy Neural Network model developed for predictions.
Achieved prediction accuracy of 95.8% for early PD detection
F-measure of 95.3%, outperforming standard machine-learning classifiers
Integration of exercise-related patterns improved classification robustness.

Vijayalakshmi et al. (2025) studied this question.

View Full Paper

Ask AI

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