ArticleHeliyon2024
A review of machine learning and deep learning algorithms for Parkinson's disease detection using handwriting and voice datasets.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Non-Invasive Voice-Based Early Detection of Parkinson's Disease via Spectral Feature Analysis and Machine Learning Techniques.Bioengineering (Basel, Switzerland) · 2026Article
- Enhancing prediction accuracy for Parkinson's disease using advanced machine learning models.Scientific reports · 2026Article
- Bio-Inspired Deep Learning for Parkinson's Disease Detection: A Comparative Study Based on Vocal Biomarkers and Archimedean Spiral Analysis.Biomimetics (Basel, Switzerland) · 2026Article
- Clinically Deployable Handwriting Biomarkers of Parkinson's Disease via Multiscale Attention and Bayesian-Genetic Optimization.Brain and behavior · 2026Article
- Neural and motor mechanisms of handwriting: from healthy aging to neurodegenerative disorders.Frontiers in aging neuroscience · 2026Review
- Computational Approaches to Neurological Disorder Diagnosis: An In-Depth Review of Current Methods and Future ProspectsCurrent medical imaging · 2026Review
- Micrographia in Parkinson's Disease: Automatic Recognition through Artificial Intelligence.Movement disorders clinical practice · 2025Article
- An Explainable Ensemble and Deep Learning Framework for Accurate and Interpretable Parkinson's Disease Detection from Voice Biomarkers.Diagnostics (Basel, Switzerland) · 2025Article
- A Deep Learning Framework for Early Parkinson's Disease Detection: Leveraging Spiral and Wave Handwriting Tasks with EfficientNetV2-S.Diagnostics (Basel, Switzerland) · 2025Article
- Voice-Based Early Diagnosis of Parkinson's Disease Using Spectrogram Features and AI Models.Bioengineering (Basel, Switzerland) · 2025Article
- In vivo and in silico models of Drosophila for Parkinson's disease.The FEBS journal · 2025Review
- Design of a deep fusion model for early Parkinson's disease prediction using handwritten image analysis.Scientific reports · 2025Article
- Explainable machine learning for early detection of Parkinson's disease in aging populations using vocal biomarkers.Frontiers in aging neuroscience · 2025Article
- Advancing Parkinson's disease detection through multi-dimensional machine learning: a comprehensive framework using wearable movement sensor analytics.Frontiers in physiology · 2025Article
- EA-CNN: Enhanced attention-CNN with explainable AI for fruit and vegetable classification.Heliyon · 2024Article
- Functional Brain Network Disruptions in Parkinson's Disease: Insights from Information Theory and Machine Learning.Diagnostics (Basel, Switzerland) · 2024Article
- Review
- Late feature fusion using neural network with voting classifier for Parkinson's disease detection.BMC medical informatics and decision making · 2024Article
- The role of artificial intelligence in drug screening, drug design, and clinical trials.Frontiers in pharmacology · 2024Review
- Toward assessing functional decline in mild cognitive impairment using wearable sensors and explainable machine learning.Digital healthArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Parkinson's Disease (PD) is a prevalent neurodegenerative disorder with significant clinical implications. Early and accurate diagnosis of PD is crucial for timely intervention and personalized treatment. In recent years, Machine Learning (ML) and Deep Learning (DL) techniques have emerged as promis-ing tools for improving PD diagnosis. This review paper presents a detailed analysis of the current state of ML and DL-based PD diagnosis, focusing on voice, handwriting, and wave spiral datasets. The study also evaluates the effectiveness of various ML and DL algorithms, including classifiers, on these datasets and highlights their potential in enhancing diagnostic accuracy and aiding clinical decision-making. Additionally, the paper explores the identifi-cation of biomarkers using these techniques, offering insights into improving the diagnostic process. The discussion encompasses different data formats and commonly employed ML and DL methods in PD diagnosis, providing a comprehensive overview of the field. This review serves as a roadmap for future research, guiding the development of ML and DL-based tools for PD detection. It is expected to benefit both the scientific community and medical practitioners by advancing our understanding of PD diagnosis and ultimately improving patient outcomes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.