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https://doi.org/10.2139/ssrn.4617895

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/mds.29362openalex-extracted
Redefining Bradykinesia
inferredcites →https://doi.org/10.1007/978-3-7908-1788-1openalex-extracted
Computational Intelligence Processing in Medical Diagnosis
inferredcites →https://doi.org/10.1007/s11761-023-00372-wopenalex-extracted
Parkinson disease prediction using machine learning-based features from speech signal
inferredcites →https://doi.org/10.1007/s12652-022-03825-wopenalex-extracted
Prediction of Parkinson’s disease based on artificial neural networks using speech datasets
inferredcites →https://doi.org/10.1007/s13534-017-0051-2openalex-extracted
Performance of machine learning methods in diagnosing Parkinson’s disease based on dysphonia measures
inferredcites →https://doi.org/10.1007/s40860-021-00141-6openalex-extracted
Machine learning approach for classification of Parkinson disease using acoustic features
inferredcites →https://doi.org/10.1016/j.asoc.2018.10.022openalex-extracted
A comparative analysis of speech signal processing algorithms for Parkinson’s disease classification and the use of the tunable Q-factor wavelet transform
inferredcites →https://doi.org/10.1016/j.jbi.2019.103362openalex-extracted
Investigating voice as a biomarker: Deep phenotyping methods for early detection of Parkinson's disease
inferredcites →https://doi.org/10.1016/j.mehy.2020.109603openalex-extracted
Early diagnosis of Parkinson’s disease using machine learning algorithms
inferredcites →https://doi.org/10.1016/j.mehy.2020.109678openalex-extracted
Parkinson disease classification using one against all based data sampling with the acoustic features from the speech signals
inferredcites →https://doi.org/10.1038/s41598-023-47568-wopenalex-extracted
A machine learning method to process voice samples for identification of Parkinson’s disease
inferredcites →https://doi.org/10.1038/s41746-024-01027-6openalex-extracted
Automatic speech-based assessment to discriminate Parkinson’s disease from essential tremor with a cross-language approach
inferredcites →https://doi.org/10.1088/1742-6596/1538/1/012024openalex-extracted
Implementation of xgboost for classification of parkinson’s disease
inferredcites →https://doi.org/10.1088/1757-899x/675/1/012031openalex-extracted
Classification of Parkinson disease data with artificial neural networks
inferredcites →https://doi.org/10.1109/esic60604.2024.10481533openalex-extracted
Early Detection of Parkinsons Using Machine Learning
inferredcites →https://doi.org/10.1109/icacat.2018.8933579openalex-extracted
Predicting the occurrence of Parkinson’s Disease using various Classification Models
inferredcites →https://doi.org/10.1109/icscds53736.2022.9760962openalex-extracted
Diagnosis of Parkinson's Disease using Principal Component Analysis and Machine Learning algorithms with Vocal Features
inferredcites →https://doi.org/10.1109/tbme.2012.2183367openalex-extracted
Novel Speech Signal Processing Algorithms for High-Accuracy Classification of Parkinson's Disease
inferredcites →https://doi.org/10.1109/uemcon54665.2022.9965732openalex-extracted
Vocal Feature Guided Detection of Parkinson’s Disease Using Machine Learning Algorithms
inferredcites →https://doi.org/10.1145/3441417.3441425openalex-extracted
A Comparative Study of Parkinson Disease Diagnosis in Machine Learning
inferredcites →https://doi.org/10.1155/2021/1765220openalex-extracted
An Investigation into the Use and Meaning of Parkinson’s Disease Clinical Scale Scores
inferredcites →https://doi.org/10.1155/2022/5524852openalex-extracted
An Efficient Rotation Forest-Based Ensemble Approach for Predicting Severity of Parkinson’s Disease
inferredcites →https://doi.org/10.18517/ijaseit.11.1.12202openalex-extracted
Development of Intelligent Parkinson Disease Detection System Based on Machine Learning Techniques Using Speech Signal
inferredcites →https://doi.org/10.21533/scjournal.v2i1.44openalex-extracted
Diagnosis of Parkinson’s Disease using Fuzzy C-Means Clustering and Pattern Recognition
inferredcites →https://doi.org/10.25080/majora-92bf1922-011openalex-extracted
Statsmodels: Econometric and Statistical Modeling with Python
inferredcites →https://doi.org/10.32996/jcsts.2023.5.4.14openalex-extracted
Parkinson's Disease Detection through Vocal Biomarkers and Advanced Machine Learning Algorithms
inferredcites →https://doi.org/10.3389/fneur.2022.831428openalex-extracted
Voice in Parkinson's Disease: A Machine Learning Study
inferredcites →https://doi.org/10.3389/frai.2023.1084001openalex-extracted
Machine learning approaches to identify Parkinson's disease using voice signal features
inferredcites →https://doi.org/10.3390/bioengineering10050531openalex-extracted
CNN-Based Identification of Parkinson’s Disease from Continuous Speech in Noisy Environments
inferredcites →https://doi.org/10.3390/diagnostics12081980openalex-extracted
An Analysis of Vocal Features for Parkinson’s Disease Classification Using Evolutionary Algorithms
inferredcites →https://doi.org/10.3390/diagnostics12082003openalex-extracted
Imperative Role of Machine Learning Algorithm for Detection of Parkinson’s Disease: Review, Challenges and Recommendations
inferredcites →https://doi.org/10.4236/jbise.2022.151002openalex-extracted
The Effect of Speech Fragmentation and Audio Encodings on Automatic Parkinson’s Disease Recognition
inferredcites →https://doi.org/10.48550/arxiv.1201.0490openalex-extracted
Scikit-learn: Machine Learning in Python
inferredcites →https://doi.org/10.48550/arxiv.2312.02229openalex-extracted
Synthetic Data Generation Techniques for Developing AI-based Speech Assessments for Parkinson's Disease (A Comparative Study)

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Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1109/ic3se62002.2024.10593626openalex-snapshot
Machine Learning Techniques for Parkinson's Disease Prediction and Progression: A Comprehensive Review
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