PD_EBM: An Integrated Boosting Approach Based on Selective Features for Unveiling Parkinson's Disease Diagnosis With Global and Local Explanations

Engineering Reports, Journal Year: 2025, Volume and Issue: 7(1)

Published: Jan. 1, 2025

ABSTRACT Early detection and characterization are crucial for treating managing Parkinson's disease (PD). The increasing prevalence of PD its significant impact on the motor neurons brain impose a substantial burden healthcare system. Early‐stage is vital improving patient outcomes reducing costs. This study introduces an ensemble boosting machine, termed PD_EBM, PD. PD_EBM leverages machine learning (ML) algorithms hybrid feature selection approach to enhance diagnostic accuracy. While ML has shown promise in medical applications detection, interpretability these models remains challenge. Explainable (XML) addresses this by providing transparency clarity model predictions. Techniques such as Local Interpretable Model‐agnostic Explanations (LIME) SHapley Additive exPlanations (SHAP) have become popular interpreting models. Our experiment used dataset 195 clinical records patients from University California Irvine (UCI) Machine Learning repository. Comprehensive data preparation included encoding categorical features, imputing missing values, removing outliers, addressing imbalance, scaling data, selecting relevant so on. We propose framework that focuses most important features prediction. employs Decision Tree (DT) classifier with AdaBoost, followed linear discriminant analysis (LDA) optimizer, achieving impressive accuracy 99.44%, outperforming other

Language: Английский

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Exploring Multimodal Framework of Optimized Feature-Based Machine Learning to Revolutionize the Diagnosis of Parkinson’s Disease: AI-Driven Insights

Deleted Journal, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 24, 2025

Language: Английский

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A lightweight CNN-based ensemble approach for early detecting Parkinson’s disease with enhanced features

International Journal of Speech Technology, Journal Year: 2025, Volume and Issue: unknown

Published: April 7, 2025

Language: Английский

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