AI and Neurological Rare & Neuroimmune Diseases ── Parkinson's ── 2026-11-07

Journals covered and how papers are chosen: see the index

Parkinson's disease and parkinsonism: treatment, trials and AI

• Reproducibility Package for: Validation, Reproducibility, and Generalization of Machine Learning Models with Wearable Sensors for Parkinson's Disease Movement Analysis: A Systematic Review
  Figshare · 2026-11-05 · Evidence: Supplementary reproducibility package for a systematic review (qualitative synthesis)
  Abstract

  This repository contains the complete reproducibility package for the systematic review titled "Validation, Reproducibility, and Generalization of Machine Learning Models with Wearable Sensors for Parkinson's Disease Movement Analysis: A Systematic Review". Following the PRISMA 2020 guidelines, this review screened 132 records from four databases (Scopus, IEEE Xplore, ACM Digital Library, and ScienceDirect) and included 11 primary studies in the qualitative synthesis. This package provides full transparency and enables the replication of our methodology. It includes:- Table S1: Complete search strings for each database.- Table S2: Detailed eligibility criteria.- Table S3: Quality assessment scores (Downs and Black adapted criteria) for all 54 full-text screened studies.- Table S4: Detailed data extraction table for the included studies.- Table S5: List of the 11 secondary studies removed from the main synthesis.- Table S6: Complete list of excluded studies with reasons.- PRISMA 2020 Checklist and PRISMA Flow Diagram.- Analysis scripts (Python/R) used for data visualization and descriptive statistics. This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

  Journal IF: unknown (could not be matched)
  Reference: [No authors listed]. Reproducibility Package for: Validation, Reproducibility, and Generalization of Machine Learning Models with Wearable Sensors for Parkinson's Disease Movement Analysis: A Systematic Review. Figshare. [Epub ahead of print]. doi:10.6084/m9.figshare.34918647.
  Checked: Abstract only
• IFI-ML: a novel framework combining Fuzzy Inference and Machine Learning for Parkinson's disease classification.
  Frontiers in artificial intelligence · 2026-10-21 · Evidence: Preclinical (retrospective computational method development and validation on public datasets)
  Abstract

  Early detection of Parkinson's Disease (PD) remains challenging due to vocal signal complexity and severe data imbalance, which necessitates that machine learning (ML) models deliver higher classification accuracy. This study aims to break the systematic, inverse trade-off between sensitivity and specificity in voice-based PD classification. We present the Intelligent Fuzzy Inference and Machine Learning (IFI-ML) framework, an uncertainty-aware architecture utilizing entropy-based partitioning, hybrid classifiers, and a Sugeno Type-2 Fuzzy Inference System (FIS). While traditional frameworks apply global resampling techniques such as the Synthetic Minority Over-sampling Technique (SMOTE) or Random Over-Sampling (ROS) across entire datasets, the proposed IFI-ML framework utilizes information gain-driven clustering to segment samples into sensitive (uncertain boundary) and non-sensitive partitions. The framework optimizes conventional ML classifiers on the stable, non-sensitive clusters, while routing critical boundary samples to the Type-2 FIS to handle local class overlap without distorting the overall data distribution. We evaluated our framework on two public, imbalanced datasets from the University of California, Irvine (UCI) Machine Learning repository: Parkinson's voice Dataset 1 ( D 1 ) and Dataset 2 ( D 2 ). The proposed framework achieves an accuracy of 97% on D 1 and 92.17% on D 2 , while simultaneously raising specificity to 94-95% on D 1 and 75-88% on D 2 . The Matthews Correlation Coefficient (MCC) improves to 0.93 and 0.77, respectively. Furthermore, IFI-ML reduces false-positive and false-negative rates to 3-7% on D 1 across all classifiers, and limits false-positive rates to 11-12% for the K-Nearest Neighbors (KNN) model on D 2 . Under a rigorous 10-fold cross-validation (10 CV) scheme, the framework consistently outperforms 15 baseline machine learning models. These benchmark results show the clinical reliability and robustness of the proposed approach, establishing a state-of-the-art benchmark for non-invasive vocal biomarkers.

  Journal IF-equivalent: 6.0 (OpenAlex 2-year mean citedness, value as of 2026-10-09, retrieved 2026-10-10; not the official Clarivate IF)
  Reference: S. V, G. M. IFI-ML: a novel framework combining Fuzzy Inference and Machine Learning for Parkinson's disease classification. Front Artif Intell. 2026 Oct 21;9:1949960. doi:10.3389/frai.2026.1949960.
  Checked: Abstract only

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