Khoshnevis SA, Sankar R. Diagnosis of Parkinson's disease using higher order statistical analysis of alpha and beta rhythms. Biomed Signal Process Control. 2022;77. https://doi.org/10.1016/j.bspc.2022.103743.

Junaid M, Ali S, Eid F, El-Sappagh S, Abuhmed T. Explainable machine learning models based on multimodal time-series data for the early detection of Parkinson’s disease. Comput Methods Programs Biomed. 2023;234:107495. https://doi.org/10.1016/j.cmpb.2023.107495.

Di Lazzaro G, Ricci M, Al-Wardat M, Schirinzi T, Scalise S, Giannini F, Mercuri NB, Saggio G, Pisani A. Technology-Based Objective Measures Detect Subclinical Axial Signs in Untreated, de novo Parkinson’s Disease. J Parkinsons Dis. 2020;10(1):113–22. https://doi.org/10.3233/JPD-191758.

Article  Google Scholar 

Ghaderyan P, Ghoreshi Beyrami SM. Neurodegenerative diseases detection using distance metrics and sparse coding: A new perspective on gait symmetric features. Comput Biol Med. 2020;120. https://doi.org/10.1016/j.compbiomed.2020.103736.

Farashi S. Distinguishing between Parkinson's disease patients and healthy individuals using a comprehensive set of time, frequency and time-frequency features extracted from vertical ground reaction force data. Biomed Signal Process Control 2020;62.

Lamba R, Gulati T, Al-Dhlan KA, Jain A. A systematic approach to diagnose Parkinson’s disease through kinematic features extracted from handwritten drawings. J Reli Intell Environ. 2021;7(3):253–62. https://doi.org/10.1007/s40860-021-00130-9.

Article  Google Scholar 

Aouraghe I, Alae A, Ghizlane K, Mrabti M, Aboulem G, Faouzi B. A novel approach combining temporal and spectral features of Arabic online handwriting for Parkinson's disease prediction. J Neurosci Methods 2020;339. https://doi.org/10.1016/j.jneumeth.2020.108727.

Karaman O, Çakın H, Alhudhaif A, Polat K. Robust automated Parkinson disease detection based on voice signals with transfer learning. Expert Sys Appl. 2021;178. https://doi.org/10.1016/j.eswa.2021.115013.

Folador JP, Santos MCS, Luiz LMD, de Souza LAPS, Vieira MF, Pereira AA, de Oliveira AA. On the use of histograms of oriented gradients for tremor detection from sinusoidal and spiral handwritten drawings of people with Parkinson’s disease. Med Biol Eng Comput. 2021;59(1):195–214. https://doi.org/10.1007/s11517-020-02303-9.

Article  Google Scholar 

Borzì L, Olmo G, Artusi CA, Fabbri M, Rizzone MG, Romagnolo A, Zibetti M, Lopiano L. A new index to assess turning quality and postural stability in patients with Parkinson's disease. Biomed Signal Process Control 2020;62. https://doi.org/10.1016/j.bspc.2020.102059.

Tjaden K. Speech and Swallowing in Parkinson’s Disease. Top Geriatr Rehabil. 2008;24(2):115–26. https://doi.org/10.1097/01.TGR.0000318899.87690.44.

Article  Google Scholar 

Vandana VP, Darshini JK, Vikram VH, Nitish K, Kumar PP, Ravi Y. Speech Characteristics of Patients with Parkinson’s Disease-Does Dopaminergic Medications Have a Role? J Neurosci Rural Pract. 2021;12(4):673–9. https://doi.org/10.1055/s-0041-1735249.

Article  Google Scholar 

Khaire UM, Dhanalakshmi R. Stability of feature selection algorithm: A review. J King Saud Univ Comput Inf Sci. 2022;34(4):1060–1073. https://doi.org/10.1016/j.jksuci.2019.06.012.

Braga D, Madureira AM, Coelho L, Ajith R. Automatic detection of Parkinson’s disease based on acoustic analysis of speech. Eng Appl Artif Intell. 2019;77:148–58. https://doi.org/10.1016/j.engappai.2018.09.018.

Article  Google Scholar 

Benba A, Jilbab A, Hammouch A. Analysis of multiple types of voice recordings in cepstral domain using MFCC for discriminating between patients with Parkinson’s disease and healthy people. Int J Speech Technol. 2016;19:449–56. https://doi.org/10.1007/s10772-016-9338-4.

Article  Google Scholar 

Sakar BE, Isenkul ME, Sakar CO, Sertbas A, Gurgen F, Delil S, Apaydin H, Kursun O. Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings. IEEE J Biomed Health Inform. 2013;17(4):828–34. https://doi.org/10.1109/jbhi.2013.2245674.

Article  Google Scholar 

Khaskhoussy R, Ayed YB. Automatic Detection of Parkinson’s Disease from Speech Using Acoustic, Prosodic and Phonetic Features. International Conference on Intelligent Systems Design and Applications. Springer, Cham. 2019;1181. https://doi.org/10.1007/978-3-030-49342-4_8.

Solana-Lavalle G, Rosas-Romero R. Analysis of voice as an assisting tool for detection of Parkinson's disease and its subsequent clinical interpretation. Biomed Signal Process Control. 2021;66. https://doi.org/10.1016/j.bspc.2021.102415.

Gómez-Rodellar A, Palacios-Alonso D, Ferrández Vicente JM, Mekyska J, Álvarez-Marquina A, Gómez-Vilda P. A Methodology to Differentiate Parkinson’s Disease and Aging Speech Based on Glottal Flow Acoustic Analysis. Int J Neural Syst. 2020;30(10):2050058. https://doi.org/10.1142/S0129065720500586.

Article  Google Scholar 

Meghraoui D, Boudraa B, Merazi-Meksen T, Vilda PG. A novel pre-processing technique in pathologic voice detection: Application to Parkinson's disease phonation. Biomed Signal Process Control. 2021;68. https://doi.org/10.1016/j.bspc.2021.102604.

Gunduz H. An efficient dimensionality reduction method using filter-based feature selection and variational autoencoders on Parkinson's disease classification. Biomed Signal Process Control. 2021;66. https://doi.org/10.1016/j.bspc.2021.102452.

Polat K. A hybrid approach to Parkinson disease classification using speech signal: The combination of SMOTE and random forests. Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT). IEEE. 2019. https://doi.org/10.1109/EBBT.2019.8741725.

Vuttipittayamongkol P, Elyan E. Improved Overlap-based Undersampling for Imbalanced Dataset Classification with Application to Epilepsy and Parkinson's Disease. Int J Neural Syst. 2020;30(8). https://doi.org/10.1142/s0129065720500434.

Benba A, Jilbab A, Hammouch A. Voice assessments for detecting patients with Parkinson’s diseases using PCA and NPCA. Int J Speech Technol. 2016;19:743–54. https://doi.org/10.1007/s10772-016-9367-z.

Article  Google Scholar 

Shahbakhti M, Taherifar D, Zareei Z. Combination of PCA and SVM for diagnosis of Parkinson's disease. 2013 2nd International Conference on Advances in Biomedical Engineering, Tripoli, Lebanon. 2013;137–140. https://doi.org/10.1109/ICABME.2013.6648866.

Shirvan RA, Tahami E, Voice analysis for detecting Parkinson’s disease using genetic algorithm and KNN classification method. 18th Iranian Conference of Biomedical Engineering (ICBME). IEEE. 2011;2011:278–83. https://doi.org/10.1109/ICBME.2011.6168572.

Article  Google Scholar 

Alalayah KM, Senan EM, Atlam HF, Ahmed IA, Shatnawi HSA. Automatic and Early Detection of Parkinson’s Disease by Analyzing Acoustic Signals Using Classification Algorithms Based on Recursive Feature Elimination Method. Diagnostics. 2023;13(11):1924. https://doi.org/10.3390/diagnostics13111924.

Article  Google Scholar 

Das R. A comparison of multiple classification methods for diagnosis of Parkinson disease. Expert Syst Appl. 2010;37(2):1568–72. https://doi.org/10.1016/j.eswa.2009.06.040.

Article  Google Scholar 

Xuchen Z, Yong F, Peng W. Automatically Predicting Severity of Parkinson's Disease Using Model Based on XGBoost from Speech, 2019 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), Dalian, China. 2019;1–5. https://doi.org/10.1109/ICSPCC46631.2019.8960722.

Elreedy D, Atiya AF. A Comprehensive Analysis of Synthetic Minority Oversampling Technique (SMOTE) for handling class imbalance. Inform Sci. 2019;505:32–64. https://doi.org/10.1016/j.ins.2019.07.070.

Article  Google Scholar 

Demir N, Kuncan M, Kaya Y, Kuncan F. Multi-Layer Co-Occurrence Matrices for Person Identification from ECG Signals. Traitement du Signal. 2022;39(2):431–40.

Article  Google Scholar 

Bhadra S, Kumar CJ. An insight into diagnosis of depression using machine learning techniques: a systematic review. Curr Med Res Opin. 2022;38(5):749–71.

Article  Google Scholar 

Anusha B, Geetha P, Kannan A. Parkinson’s disease identification in homo sapiens based on hybrid ResNet-SVM and resnet-fuzzy svm models. J Intell Fuzzy Syst. 2022;43(3):2711–29.

Article  Google Scholar 

Toma M, Wei OC. Predictive Modeling in Medicine. Encyclopedia. 2023;3(2):590–601. https://doi.org/10.3390/encyclopedia3020042.

Article  Google Scholar 

Gupta R, Kumari S, Senapati A, Ambasta RK, Kumar P. New era of artificial intelligence and machine learning-based detection, diagnosis, and therapeutics in Parkinson’s disease. Ageing Res Rev. 2023;90:102013. https://doi.org/10.1016/j.arr.2023.102013.

Latha S, Samiappan D. Despeckling of carotid artery ultrasound images with a calculus approach. Curr Med Imaging. 2019;15(4):414–26. https://doi.org/10.2174/1573405614666180402124438.

Article  Google Scholar 

Aşuroğlu T, Oğul H. A deep learning approach for parkinson’s disease severity assessment. Health Technol. 2022;12:943–53. https://doi.org/10.1007/s12553-022-00698-z.

Article  Google Scholar 

Ertuğrul ÖF, Kaya Y, Tekin R, Almalı MN. Detection of Parkinson’s disease by shifted one dimensional local binary patterns from gait. Expert Syst Appl. 2016;56:156–63.

Article  Google Scholar 

Özel E, Tekin R, Kaya Y. Implementation of Artifact Removal Algorithms in Gait Signals for Diagnosis of Parkinson Disease. Trait du Signal. 2021;38(3):587–597. https://doi.org/10.18280/ts.380306.

Singh KR, Dash S. Chapter 1 - Early detection of neurological diseases using machine learning and deep learning techniques: A review. Artificial Intelligence for Neurological Disorders, Academic Press. 2022;1–24. https://doi.org/10.1016/B978-0-323-90277-9.00001-8.

Ayaz Z, Naz S, Khan NH, Razzak I, Imran M. Automated methods for diagnosis of Parkinson’s disease and predicting severity level. Neural Comput Appl. 2023;35(20):14499–534. https://doi.org/10.1007/s00521-021-06626-y.

Article  Google Scholar 

Xu Z, Shen D, Nie T, Kou Y. A hybrid sampling algorithm combining M-SMOTE and ENN based on Random forest for medical imbalanced data. J Biomed Inform. 2020;107:103465. https://doi.org/10.1016/j.jbi.2020.103465.

Lamba R, Gulati T, Alharbi HF, Jain A. A hybrid system for Parkinson’s disease diagnosis using machine learning techniques. Int J Speech Technol. 2022;25(3):583–93. https://doi.org/10.1007/s10772-021-09837-9.

Article  Google Scholar 

Gunduz H. Deep learning-based Parkinson’s disease classification using vocal feature sets. IEEE Access. 2019;7:115540–51.

Article  Google Scholar 

Pramanik M. Pradhan R, Nandy P, Qaisar SM, Bhoi AK. Assessment of Acoustic Features and Machine Learning for Parkinson’s Detection. J Healthc Eng. 2021;9957132. https://doi.org/10.1155/2021/9957132.

Parisi L, Ma R, Zaernia A, Youseffi M. m-ark-Support Vector Machine for Early Detection of Parkinson’s Disease from Speech Signals. Int J Math Comp Sim. 2021;15:34–41. https://doi.org/10.46300/9102.2021.15.7.

Mittal V, Sharma RK. Machine learning approach for classification of Parkinson disease using acoustic features. J Reliab Intell Environ. 2021;7:233–9. https://doi.org/10.1007/s40860-021-00141-6.

Article  Google Scholar 

Parlar T. A heuristic approach with artificial neural network for Parkinson’s disease. Int J Appl Math Elec Comp. 2021;9(1):1–6. https://doi.org/10.18100/ijamec.802599.

Anisha CD, Arulanand N. Early Prediction of Parkinson's Disease (PD) Using Ensemble Classifiers. 2020 International Conference on Innovative Trends in Information Technology (ICITIIT), Kottayam, India. 2020;1–6. https://doi.org/10.1109/ICITIIT49094.2020.9071562.

Hasan KA, Hasan AM, M. Classification of Parkinson’s Disease by Analyzing Multiple Vocal Features Sets,. IEEE Region 10 Symposium (TENSYMP). Dhaka, Bangladesh. 2020;2020:758–61. https://doi.org/10.1109/TENSYMP50017.2020.9230842.

Article  Google Scholar 

Hema MS, Maheshprabhu R, Reddy KS, Guptha MN, Pandimurugan V. Prediction analysis for Parkinson disease using multiple feature selection & classification methods. Multimed Tools Appl. 2023;82(27):42995–3012. https://doi.org/10.1007/s11042-023-15280-6.

Article  Google Scholar 

Alshammri R, Alharbi G, Alharbi E, Almubark I. Machine learning approaches to identify Parkinson’s disease using voice signal features. Front Artif Intell. 2023;6:1084001. https://doi.org/10.3389/frai.2023.1084001.

Article  Google Scholar 

Celik G, Başaran E. Proposing a new approach based on convolutional neural networks and random forest for the diagnosis of Parkinson’s disease from speech signals. Appl Acoust. 2023;211:109476. https://doi.org/10.1016/j.apacoust.2023.109476.

Ouhmida A, Raihani A, Cherradi B, Sandabad S. Parkinson’s diagnosis hybrid system based on deep learning classification with imbalanced dataset. Int J Electr Comput Eng. 2023;13(3):3204–3216. https://doi.org/10.11591/ijece.v13i3.pp3204-3216.

Balakrishnan A, Medikonda J, Namboothiri PK, Natarajan M. Mahalanobis Metric-based Oversampling Technique for Parkinson’s Disease Severity Assessment using Spatiotemporal Gait Parameters. Biomed Signal Process Control. 2023;86:105057. https://doi.org/10.1016/j.bspc.2023.105057.