Research map: CMFS-LSML: Consensus-Based multi-metaheuristic feature selection with lightweight supervised machine learning for enhanced Parkinson disease classification from voice features

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  1. Antiageing strategy for neurodegenerative diseases: from mechanisms to clinical advances · Qiu Jiang · 2025 · 152 citations · Cited by this paper
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  15. Parkinson’s disease Detection and Classification: Leveraging Voice Features and Ensemble Methods with Feature Selection and ERT Classifier · P. Deepa · 2024 · 14 citations · Cited by this paper
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  17. Novel Voice Signal Segmentation Based on Clark Distance to Improve Intelligent Parkinson Disease Detection · Mostafa Abdulghafoor Mohammed · 2025 · 11 citations · Cited by this paper
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  19. Improving detection of Parkinson’s disease with acoustic feature optimization using particle swarm optimization and machine learning · Elmoundher Hadjaidji · 2025 · 9 citations · Cited by this paper
  20. Brain State Transition Disruptions in Alzheimer's Disease: Insights from EEG State Dynamics · S.M.R. Modarres Razavi · 2025 · 8 citations · Cited by this paper
  21. Efficient clustering approach based on Gower distance for high-dimensional medical datasets · Salwa Shakir Baawi · 2025 · 5 citations · Cited by this paper
  22. DWT-OEFS: discrete wavelet transform based optimized ensemble feature selection for Parkinson’s disease severity classification · Sneha Agrawal · 2025 · 1 citation · Cited by this paper
  23. Decision Trees · Karol Przystalski · 2025 · 1 citation · Cited by this paper
  24. Novel ECG signal classification based on Minkowski distance to enhance intelligent arrhythmia detection systems · Rawaa R. Rfys · 2026 · 1 citation · Cited by this paper
  25. A modified golden jackal optimization framework for high-dimensional feature selection in Parkinson’s Disease classification · Bikram Keshari Kar · 2026 · 1 citation · Cited by this paper
  26. Efficient Voice-Based Parkinson Classification via Algorithm-Level Class Balancing · Zahraa Ch. Oleiwi · 2026 · 1 citation · Cited by this paper

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