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The performance of various machine learning methods for Parkinson’s disease recognition: a systematic review

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Abstract

Parkinson’s disease (PD) is a common neurodegenerative disorder that causes degeneration of dopaminergic neurons in the Nigrostriatal pathway and the discharge of Dopamine in the striatum. Machine learning algorithms have been used as a tool to predict and diagnose diseases. Some of these algorithms got the popularity due to their high recognition performance. In the recognition of PD, studies demonstrated various recognition performances and this systematic review study the performance of machine learning algorithms. This systematic review is based on the cochrane’s proposed seven phases of review. After identifying the question of research and inclusion/exclusion criteria, we searched different related databases (SID, MagIran, PubMed, ProQuest, ScienceDirect, WoS, Scopus, and Google Scholar) with the help of combination of keywords. After selection of the studies we extract information and summarize the results. From 10,980 found-studies, and removing them based on inclusion/exclustion criteria, we selected 82 studies. To diagnose PD, 59 studies used clinical indicators, 2 studies used genetic characteristics, 12 used MRI, two used PET, 5 used SPECT and 2 used Laboratory markers. In most of these studies RF, SVM, LR have performed the best. The accuracies of RF, SVM, and LR are reported between 58.9%-99.42%, 65.2%-99.99%, and 43.9%-96%. The results show that the performance of RF, SVM, and LR are high for PD diagnosis. Therefore, they can be used for PD diagnosis as a help for doctors and specialist.

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Availability of Data and Materials

Datasets are available through the corresponding author upon reasonable request.

Abbreviations

PD:

Parkinson’s disease

ML:

Machine learning

DT:

Decision Tree

RF:

Random Forst

K-NN:

K-Nearest Neighbors

LDA:

Linear Discriminant Analysis

LR:

Logistic Regression

SVM:

Support Vector Machine

NB:

Na¨ıve Bayes

References

  • Abós, A., Baggio, H. C., Segura, B., García-Díaz, A. I., Compta, Y., Martí, M. J., Valldeoriola, F., & Junqué, C. (2017). Discriminating cognitive status in Parkinson’s disease through functional connectomics and machine learning. Scientific Reports, 7, 1–13.

    Article  Google Scholar 

  • Abujrida, H., Agu, E., & Pahlavan, K. (2020). Machine learning-based motor assessment of Parkinson’s disease using postural sway, gait and lifestyle features on crowdsourced smartphone data. Biomedical Physics & Engineering Express, 6, 035005.

    Article  Google Scholar 

  • Agarwal, A., Chandrayan, S. & Sahu, S. S. (2016). Prediction of Parkinson's disease using speech signal with Extreme Learning Machine. 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), IEEE, 3776-3779.

  • Ahlrichs, C. & Lawo, M. (2013). Parkinson's disease motor symptoms in machine learning: A review. arXiv preprint arXiv:1312.3825.

  • Aich, S., Pradhan, P. M., Park, J., & Kim, H.-C. (2018). A machine learning approach to distinguish Parkinson’s disease (PD) patient’s with shuffling gait from older adults based on gait signals using 3D motion analysis. Intenational Journal of Enginering Technology, 7, 153–156.

    Google Scholar 

  • Aich, S., Youn, J., Chakraborty, S., Pradhan, P. M., Park, J.-H., Park, S., & Park, J. (2020). A supervised machine learning approach to detect the on/off state in Parkinson’s disease using wearable based gait signals. Diagnostics, 10, 421.

    Article  PubMed  PubMed Central  Google Scholar 

  • Alam, M. N., Garg, A., Munia, T. T. K., Fazel-Rezai, R., & Tavakolian, K. (2017). Vertical ground reaction force marker for Parkinson’s disease. PLoS One, 12, e0175951.

    Article  PubMed  PubMed Central  Google Scholar 

  • Ali, L., Zhu, C., Zhang, Z., & Liu, Y. (2019). Automated detection of Parkinson’s disease based on multiple types of sustained phonations using linear discriminant analysis and genetically optimized neural network. IEEE Journal of Translational Engineering in Health and Medicine, 7, 1–10.

    Article  Google Scholar 

  • Almeida, J. S., Rebouças Filho, P. P., Carneiro, T., Wei, W., Damaševičius, R., Maskeliūnas, R., & de Albuquerque, V. H. C. (2019). Detecting Parkinson’s disease with sustained phonation and speech signals using machine learning techniques. Pattern Recognition Letters, 125, 55–62.

    Article  Google Scholar 

  • Armañanzas, R., Bielza, C., Chaudhuri, K. R., Martinez-Martin, P., & Larrañaga, P. (2013). Unveiling relevant non-motor Parkinson's disease severity symptoms using a machine learning approach. Artificial Intelligence in Medicine, 58, 195–202.

    Article  PubMed  Google Scholar 

  • Avci, D. & Dogantekin, A. (2016). An expert diagnosis system for parkinson disease based on genetic algorithm-wavelet kernel-extreme learning machine. Parkinson’s Disease, 2016.

  • Azuma, T., Cruz, R. F., Bayles, K. A., Tomoeda, C. K., & Montgomery Jr., E. B. (2003). A longitudinal study of neuropsychological change in individuals with Parkinson's disease. International Journal of Geriatric Psychiatry, 18, 1043–1049.

    Article  PubMed  Google Scholar 

  • Baghoori, D., Rezaei, M., Mirzakhani, N., Irani, A., & Baghban, A. A. (2016). Face and content validity of Persian version of Parkinson fatigue scale (PFS-16) in people with Parkinson’s disease. Journal of Rehabilitation Medicine, 5, 85–92.

    Google Scholar 

  • Buongiorno, D., Bortone, I., Cascarano, G. D., Trotta, G. F., Brunetti, A., & Bevilacqua, V. (2019). A low-cost vision system based on the analysis of motor features for recognition and severity rating of Parkinson’s disease. BMC Medical Informatics and Decision Making, 19, 1–13.

    Article  Google Scholar 

  • Byeon, H. (2020). Is the random forest algorithm suitable for predicting parkinson’s disease with mild cognitive impairment out of parkinson’s disease with normal cognition? International Journal of Environmental Research and Public Health, 17, 2594.

    Article  PubMed  PubMed Central  Google Scholar 

  • Caesarendra, W., Ariyanto, M., Setiawan, J. D., Arozi, M., & Chang, C. R. (2014). A pattern recognition method for stage classification of Parkinson's disease utilizing voice features. 2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES). IEEE, 87-92.

  • Caesarendra, W., Putri, F. T., Ariyanto, M. & Setiawan, J. D. (2015). Pattern recognition methods for multi stage classification of Parkinson's disease utilizing voice features. 2015 IEEE International Conference on Advanced Intelligent Mechatronics (AIM). IEEE, 802-807.

  • Cantürk, İ., & Karabiber, F. (2016). A machine learning system for the diagnosis of Parkinson’s disease from speech signals and its application to multiple speech signal types. Arabian Journal for Science and Engineering, 41, 5049–5059.

    Article  Google Scholar 

  • Cao, X., Wang, X., Xue, C., Zhang, S., Huang, Q., & Liu, W. (2020). A radiomics approach to predicting Parkinson’s disease by incorporating whole-brain functional activity and gray matter structure. Frontiers in Neuroscience, 14.

  • Capecci, M., Ciabattoni, L., Foresi, G., Monteriù, A. & Pepa, L. (2019). A Machine-Learning Based Emotion Recognition System in Patients with Parkinson’s Disease. 2019 IEEE 9th International Conference on Consumer Electronics (ICCE-Berlin). IEEE, 20-21.

  • Castillo-Barnes, D., Ramírez, J., Segovia, F., Martínez-Murcia, F. J., Salas-Gonzalez, D., & Górriz, J. M. (2018). Robust ensemble classification methodology for I123-Ioflupane SPECT images and multiple heterogeneous biomarkers in the diagnosis of Parkinson's disease. Frontiers in Neuroinformatics, 12, 53.

    Article  PubMed  PubMed Central  Google Scholar 

  • Cavallo, F., Moschetti, A., Esposito, D., Maremmani, C., & Rovini, E. (2019). Upper limb motor pre-clinical assessment in Parkinson's disease using machine learning. Parkinsonism & Related Disorders, 63, 111–116.

    Article  Google Scholar 

  • Centi, J., Freeman, R., Gibbons, C. H., Neargarder, S., Canova, A. O., & Cronin-Golomb, A. (2017). Effects of orthostatic hypotension on cognition in Parkinson disease. Neurology, 88, 17–24.

    Article  PubMed  PubMed Central  Google Scholar 

  • Chahine, L. M., Weintraub, D., Hawkins, K. A., Siderowf, A., Eberly, S., Oakes, D., Seibyl, J., Stern, M. B., Marek, K., & Jennings, D. (2016). Cognition in individuals at risk for Parkinson's: Parkinson associated risk syndrome (PARS) study findings. Movement Disorders, 31, 86–94.

    Article  PubMed  Google Scholar 

  • Challa, K. N. R., Pagolu, V. S., Panda, G., & Majhi, B. (2016). An improved approach for prediction of Parkinson's disease using machine learning techniques. 2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES). IEEE, 1446-1451.

  • Chen, H.-L., Wang, G., Ma, C., Cai, Z.-N., Liu, W.-B., & Wang, S.-J. (2016). An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson’ s disease. Neurocomputing, 184, 131–144.

    Article  Google Scholar 

  • Chén, O. Y., Lipsmeier, F., Phan, H., Prince, J., Taylor, K. I., Gossens, C., Lindemann, M., & de Vos, M. (2020). Building a machine-learning framework to remotely assess parkinson’s disease using smartphones. IEEE Transactions on Biomedical Engineering, 67, 3491–3500.

    Article  PubMed  Google Scholar 

  • Chen, Y., Zhu, G., Liu, D., Liu, Y., Yuan, T., Zhang, X., Jiang, Y., Du, T., & Zhang, J. (2020). The morphology of thalamic subnuclei in Parkinson's disease and the effects of machine learning on disease diagnosis and clinical evaluation. Journal of the Neurological Sciences, 411, 116721.

    Article  PubMed  Google Scholar 

  • Cheng, Z., Zhang, J., He, N., Li, Y., Wen, Y., Xu, H., Tang, R., Jin, Z., Haacke, E. M., & Yan, F. (2019). Radiomic features of the nigrosome-1 region of the substantia nigra: Using quantitative susceptibility mapping to assist the diagnosis of idiopathic Parkinson's disease. Frontiers in Aging Neuroscience, 11, 167.

    Article  PubMed  PubMed Central  Google Scholar 

  • De Souza, J. W., Alves, S. S., Rebouças, E. D. S., Almeida, J. S., & Rebouças Filho, P. P. (2018). A new approach to diagnose parkinson’s disease using a structural cooccurrence matrix for a similarity analysis. Computational Intelligence and Neuroscience, 2018.

  • De Vos, M., Prince, J., Buchanan, T., Fitzgerald, J. J., & Antoniades, C. A. (2020). Discriminating progressive supranuclear palsy from Parkinson's disease using wearable technology and machine learning. Gait & Posture, 77, 257–263.

    Article  Google Scholar 

  • Devrome, M., van Weehaeghe, D., de Vocht, J., van Damme, P., van Laere, K., & Koole, M. (2018). Glucose metabolic brain patterns to discriminate amyotrophic lateral sclerosis from Parkinson plus syndromes. EJNMMI Research, 8, 1–10.

    Article  Google Scholar 

  • Dinov, I. D., Heavner, B., Tang, M., Glusman, G., Chard, K., Darcy, M., Madduri, R., PA, J., Spino, C., & Kesselman, C. (2016). Predictive big data analytics: A study of Parkinson’s disease using large, complex, heterogeneous, incongruent, multi-source and incomplete observations. PLoS One, 11, e0157077.

    Article  PubMed  PubMed Central  Google Scholar 

  • Duque, J. D. L., Egea, A. J. S., Reeb, T., Rojas, H. A. G., & Gonzalez-Vargas, A. M. (2020). Angular velocity analysis boosted by machine learning for helping in the differential diagnosis of Parkinson’s disease and essential tremor. IEEE Access, 8, 88866–88875.

    Article  Google Scholar 

  • Eshraghi-Jazi, F., Alaei, H., Azizi-Malekabadi, H., Gharavi-Naini, M., & Pilehvarian, A. (2012). The effect of red grape juice and exercise, and their combination on parkinson’s disease in rats. Avicenna Journal of Phytomedicine, 2, 90.

    PubMed  PubMed Central  Google Scholar 

  • Eskofier, B. M., Lee, S. I., Daneault, J.-F., Golabchi, F. N., Ferreira-Carvalho, G., Vergara-Diaz, G., Sapienza, S., Costante, G., Klucken, J., & Kautz, T. (2016). Recent machine learning advancements in sensor-based mobility analysis: Deep learning for Parkinson's disease assessment. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 655-658.

  • Fung, G., Deng, Y., Zhao, Q., Li, Z., Qu, M., Li, K., Zeng, Y.-W., Jin, Z., Ma, Y.-T., & Yu, X. (2015). Distinguishing bipolar and major depressive disorders by brain structural morphometry: A pilot study. BMC Psychiatry, 15, 1–12.

    Article  Google Scholar 

  • Gao, C., Sun, H., Wang, T., Tang, M., Bohnen, N. I., Müller, M. L., Herman, T., Giladi, N., Kalinin, A., & Spino, C. (2018). Model-based and model-free machine learning techniques for diagnostic prediction and classification of clinical outcomes in Parkinson’s disease. Scientific Reports, 8, 1–21.

    Google Scholar 

  • Gautam, R., & Sharma, M. (2020). Prevalence and diagnosis of neurological disorders using different deep learning techniques: A meta-analysis. Journal of Medical Systems, 44, 1–24.

    Article  Google Scholar 

  • Goes, A., Souza, L., del Fabbro, L., de Gomes, M., Boeira, S., & Jesse, C. (2014). Neuroprotective effects of swimming training in a mouse model of Parkinson’s disease induced by 6-hydroxydopamine. Neuroscience, 256, 61–71.

    Article  PubMed  Google Scholar 

  • Goetz, C. G. (2011). The history of Parkinson's disease: Early clinical descriptions and neurological therapies. Cold Spring Harbor Perspectives in Medicine, 1, a008862.

    Article  PubMed  PubMed Central  Google Scholar 

  • Haji Pour Talebi, A., Sadeghi, M., Mehrabi, N., Gholamhoseini, L., & Gol Mohammadi, M. (2019). Explaining the challenges and solutions for implementing smart Systems in the Health System. Paramedical Sciences and Military Health, 14, 69–74.

    Google Scholar 

  • Henderson, L. K., Craig, J. C., Willis, N. S., Tovey, D., & Webster, A. C. (2010). How to write a Cochrane systematic review. Nephrology, 15, 617–624.

    Article  PubMed  Google Scholar 

  • Hosseini, M., Rajaei, Z., & Alaei, H. (2015). Effects of crocin on rotational behavior, lipid peroxidation and nitrite levels in Rat’s brain striatum in an experimental model of Parkinson’s disease. Journal of Isfahan Medical School, 33.

  • Hsu, S.-Y., Lin, H.-C., Chen, T.-B., Du, W.-C., Hsu, Y.-H., Wu, Y.-C., Tu, P.-W., Huang, Y.-H., & Chen, H.-Y. (2019). Feasible classified models for Parkinson disease from 99mTc-TRODAT-1 SPECT imaging. Sensors, 19, 1740.

    Article  PubMed  PubMed Central  Google Scholar 

  • Huang, J.-Y., Zhang, J.-R., Shen, Y., Zhang, H.-J., Cao, Y.-L., Mao, C.-J., Yang, Y.-P., Chen, J., Liu, C.-F., & Li, J. (2018). Effect of rapid eye movement sleep behavior disorder on obstructive sleep apnea severity and cognition of Parkinson's disease patients. Chinese Medical Journal, 131, 899.

    Article  PubMed  PubMed Central  Google Scholar 

  • Huertas, I., Oldehinkel, M., van Oort, E. S., Garcia-Solis, D., Mir, P., Beckmann, C. F., & Marquand, A. F. (2017). A Bayesian spatial model for neuroimaging data based on biologically informed basis functions. NeuroImage, 161, 134–148.

    Article  PubMed  Google Scholar 

  • Jeon, H., Lee, W., Park, H., Lee, H. J., Kim, S. K., Kim, H. B., Jeon, B., & Park, K. S. (2017). Automatic classification of tremor severity in Parkinson’s disease using a wearable device. Sensors, 17, 2067.

    Article  PubMed  PubMed Central  Google Scholar 

  • Jones, C. A., Hoffman, M. R., Lin, L., Abdelhalim, S., Jiang, J. J., & Mcculloch, T. M. (2018). Identification of swallowing disorders in early and mid-stage Parkinson's disease using pattern recognition of pharyngeal high-resolution manometry data. Neurogastroenterology and Motility, 30, e13236.

    Article  PubMed  Google Scholar 

  • Juutinen, M., Wang, C., Zhu, J., Haladjian, J., Ruokolainen, J., Puustinen, J., & Vehkaoja, A. (2020). Parkinson’s disease detection from 20-step walking tests using inertial sensors of a smartphone: Machine learning approach based on an observational case-control study. PLoS One, 15, e0236258.

    Article  PubMed  PubMed Central  Google Scholar 

  • Kaur, P., & Sharma, M. (2019). Diagnosis of human psychological disorders using supervised learning and nature-inspired computing techniques: A meta-analysis. Journal of Medical Systems, 43, 1–30.

    Article  Google Scholar 

  • Kim, K.-J., Noh, D.-H., Han, S.-H., Cha, Y.-J., & Kam, K.-Y. (2020). Correlation between depression and memory according to apolipoprotein E genotype in elderly with Alzheimer's dementia. Journal of the Korea Academia-Industrial cooperation Society, 21, 477–486.

    Google Scholar 

  • Klempíř, O., & Krupička, R. (2018). Machine learning using speech utterances for parkinson disease detection. Lékař a technika-Clinician and Technology, 48, 66–71.

    Google Scholar 

  • Koch, M., Geraedts, V., Wang, H., Tannemaat, M., & Bäck, T. (2019). Automated machine learning for EEG-based classification of Parkinson’s disease patients. 2019 IEEE International Conference on Big Data (Big Data). IEEE, 4845-4852.

  • Kotsavasiloglou, C., Kostikis, N., Hristu-Varsakelis, D., & Arnaoutoglou, M. (2017). Machine learning-based classification of simple drawing movements in Parkinson’s disease. Biomedical Signal Processing and Control, 31, 174–180.

    Article  Google Scholar 

  • Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., & Fotiadis, D. I. (2015). Machine learning applications in cancer prognosis and prediction. Computational and Structural Biotechnology Journal, 13, 8–17.

    Article  PubMed  Google Scholar 

  • Kumari, V. A., & Chitra, R. (2013). Classification of diabetes disease using support vector machine. International Journal of Engineering Research and Applications, 3, 1797–1801.

    Google Scholar 

  • Lahmiri, S., Dawson, D. A., & Shmuel, A. (2018). Performance of machine learning methods in diagnosing Parkinson’s disease based on dysphonia measures. Biomedical Engineering Letters, 8, 29–39.

    Article  PubMed  Google Scholar 

  • Laudon, K. C., & Laudon, J. P. (1995). Management information systems: Organization and technology. Prentice-Hall, Inc.

    Google Scholar 

  • Leclair-Visonneau, L., Clairembault, T., Coron, E., Le Dily, S., Vavasseur, F., Dalichampt, M., Péréon, Y., Neunlist, M., & Derkinderen, P. (2017). REM sleep behavior disorder is related to enteric neuropathology in Parkinson disease. Neurology, 89, 1612–1618.

    Article  PubMed  Google Scholar 

  • Litvan, I., Aarsland, D., Adler, C. H., Goldman, J. G., Kulisevsky, J., Mollenhauer, B., Rodriguez-Oroz, M. C., Tröster, A. I., & Weintraub, D. (2011). MDS task force on mild cognitive impairment in Parkinson's disease: Critical review of PD-MCI. Movement Disorders, 26, 1814–1824.

    Article  PubMed  PubMed Central  Google Scholar 

  • Lötsch, J., Haehner, A., & Hummel, T. (2020). Machine-learning-derived rules set excludes risk of Parkinson’s disease in patients with olfactory or gustatory symptoms with high accuracy. Journal of Neurology, 267, 469–478.

    Article  PubMed  Google Scholar 

  • Magesh, P. R., Myloth, R. D., & Tom, R. J. (2020). An explainable machine learning model for early detection of Parkinson's disease using LIME on DaTSCAN imagery. Computers in Biology and Medicine, 126, 104041.

    Article  PubMed  Google Scholar 

  • Mandal, I., & Sairam, N. (2014). New machine-learning algorithms for prediction of Parkinson's disease. International Journal of Systems Science, 45, 647–666.

    Article  Google Scholar 

  • Moon, S., Song, H.-J., Sharma, V. D., Lyons, K. E., Pahwa, R., Akinwuntan, A. E., & Devos, H. (2020). Classification of Parkinson's disease and essential tremor based on gait and balance characteristics from wearable motion sensors: A data-driven approach. medRxiv.

  • Moshkova, A., Samorodov, A., Voinova, N., Volkov, A., Ivanova, E., & Fedotova, E. (2020). Parkinson’s disease detection by using machine learning algorithms and hand movement signal from LeapMotion sensor. 2020 26th Conference of Open Innovations Association (FRUCT). IEEE, 321-327.

  • Mwangi, B., Wu, M.-J., Bauer, I. E., Modi, H., Zeni, C. P., Zunta-Soares, G. B., Hasan, K. M., & Soares, J. C. (2015). Predictive classification of pediatric bipolar disorder using atlas-based diffusion weighted imaging and support vector machines. Psychiatry Research: Neuroimaging, 234, 265–271.

    Article  Google Scholar 

  • Nguyen, K. P., Raval, V., Treacher, A., Mellema, C., Yu, F., Pinho, M. C., Subramaniam, R. M., Dewey, R. B., & Montillo, A. (2020). Predicting longitudinal disease severity for individuals with parkinson’s disease using functional MRI and machine learning prognostic models. bioRxiv.

  • Nissar, I., Rizvi, D., Masood, S., & Mir, A. (2019). Voice-based detection of Parkinson’s disease through ensemble machine learning approach: A Performance study. EAI Endorsed Transactions on Pervasive Health and Technology, 5.

  • Noh, Y., Sung, Y., Lee, J., & Kim, E. (2015). Nigrosome 1 detection at 3T MRI for the diagnosis of early-stage idiopathic Parkinson disease: Assessment of diagnostic accuracy and agreement on imaging asymmetry and clinical laterality. American Journal of Neuroradiology, 36, 2010–2016.

    Article  PubMed  PubMed Central  Google Scholar 

  • Omondiagbe, D. A., Veeramani, S., & Sidhu, A. S. (2019). Machine learning classification techniques for breast cancer diagnosis. IOP Conference Series: Materials Science and Engineering. IOP Publishing, 012033.

  • Patra, A. K., Ray, R., Abdullah, A. A., & Dash, S. R. (2019) . Prediction of Parkinson’s disease using Ensemble Machine Learning classification from acoustic analysis. Journal of Physics: Conference Series. IOP Publishing, 012041.

  • Pedrosa, T. Í., Vasconcelos, F. F., Medeiros, L., & Silva, L. D. (2018). Machine learning application to quantify the tremor level for parkinson’s disease patients. Procedia Computer Science, 138, 215–220.

    Article  Google Scholar 

  • Peng, B., Wang, S., Zhou, Z., Liu, Y., Tong, B., Zhang, T., & Dai, Y. (2017). A multilevel-ROI-features-based machine learning method for detection of morphometric biomarkers in Parkinson’s disease. Neuroscience Letters, 651, 88–94.

    Article  PubMed  Google Scholar 

  • Petersen, R. C., Roberts, R. O., Knopman, D. S., Boeve, B. F., Geda, Y. E., Ivnik, R. J., Smith, G. E., & Jack, C. R. (2009). Mild cognitive impairment: Ten years later. Archives of Neurology, 66, 1447–1455.

    Article  PubMed  PubMed Central  Google Scholar 

  • Postuma, R. B., Berg, D., Stern, M., Poewe, W., Olanow, C. W., Oertel, W., Obeso, J., Marek, K., Litvan, I., & Lang, A. E. (2015). MDS clinical diagnostic criteria for Parkinson’s disease. Movement Disorders, 30, 1591–1601.

    Article  PubMed  Google Scholar 

  • Prashanth, R., & Roy, S. D. (2018). Early detection of Parkinson’s disease through patient questionnaire and predictive modelling. International Journal of Medical Informatics, 119, 75–87.

    Article  PubMed  Google Scholar 

  • Prashanth, R., Roy, S. D., Mandal, P. K., & Ghosh, S. (2016a). High-accuracy classification of parkinson's disease through shape analysis and surface fitting in 123I-Ioflupane SPECT imaging. IEEE Journal of Biomedical and Health Informatics, 21, 794–802.

    Article  PubMed  Google Scholar 

  • Prashanth, R., Roy, S. D., Mandal, P. K., & Ghosh, S. (2016b). High-accuracy detection of early Parkinson’s disease through multimodal features and machine learning. International Journal of Medical Informatics, 90, 13–21.

    Article  PubMed  Google Scholar 

  • Pringsheim, T., Jette, N., Frolkis, A., & Steeves, T. D. (2014). The prevalence of Parkinson's disease: A systematic review and meta-analysis. Movement Disorders, 29, 1583–1590.

    Article  PubMed  Google Scholar 

  • Rehman, R. Z. U., Del Din, S., Guan, Y., Yarnall, A. J., Shi, J. Q., & Rochester, L. (2019a). Selecting clinically relevant gait characteristics for classification of early parkinson’s disease: A comprehensive machine learning approach. Scientific Reports, 9, 1–12.

    Article  Google Scholar 

  • Rehman, R. Z. U., Del Din, S., Shi, J. Q., Galna, B., Lord, S., Yarnall, A. J., Guan, Y., & Rochester, L. (2019b). Comparison of walking protocols and gait assessment systems for machine learning-based classification of parkinson’s disease. Sensors, 19, 5363.

    Article  PubMed  PubMed Central  Google Scholar 

  • Rezaee, Z., Marandi, S. M., Alaei, H., & Esfarjani, F. (2019). Molecular mechanisms of Parkinson’s disease. The Neuroscience Journal of Shefaye Khatam, 8, 120–128.

    Article  Google Scholar 

  • Ricciardi, C., Amboni, M., de Santis, C., Ricciardelli, G., Improta, G., Cesarelli, G., D’addio, G., & Barone, P. (2020). Classifying patients affected by Parkinson’s disease into freezers or non-freezers through machine learning. 2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA). IEEE, 1-6.

  • Rizzo, G., Copetti, M., Arcuti, S., Martino, D., Fontana, A., & Logroscino, G. (2016). Accuracy of clinical diagnosis of Parkinson disease: A systematic review and meta-analysis. Neurology, 86, 566–576.

    Article  PubMed  Google Scholar 

  • Rovini, E., Maremmani, C., Moschetti, A., Esposito, D., & Cavallo, F. (2018). Comparative motor pre-clinical assessment in Parkinson’s disease using supervised machine learning approaches. Annals of Biomedical Engineering, 46, 2057–2068.

    Article  PubMed  Google Scholar 

  • Sadoughi, F., & Sheikhtaheri, A. (2011). Applications of artificial intelligence in clinical decision making: opportunities and challenges.

  • Sajal, M. S. R., Ehsan, M. T., Vaidyanathan, R., Wang, S., Aziz, T., & Al Mamun, K. A. (2020). Telemonitoring Parkinson’s disease using machine learning by combining tremor and voice analysis. Brain Informatics, 7, 1–11.

    Article  Google Scholar 

  • Salvatore, C., Cerasa, A., Castiglioni, I., Gallivanone, F., Augimeri, A., Lopez, M., Arabia, G., Morelli, M., Gilardi, M., & Quattrone, A. (2014). Machine learning on brain MRI data for differential diagnosis of Parkinson's disease and progressive Supranuclear palsy. Journal of Neuroscience Methods, 222, 230–237.

    Article  PubMed  Google Scholar 

  • Senturk, Z. K. (2020). Early diagnosis of Parkinson’s disease using machine learning algorithms. Medical Hypotheses, 138, 109603.

    Article  Google Scholar 

  • Shamir, R. R., Dolber, T., Noecker, A. M., Walter, B. L., & Mcintyre, C. C. (2015). Machine learning approach to optimizing combined stimulation and medication therapies for Parkinson's disease. Brain Stimulation, 8, 1025–1032.

    Article  PubMed  PubMed Central  Google Scholar 

  • Shen, L., Chen, H., Yu, Z., Kang, W., Zhang, B., Li, H., Yang, B., & Liu, D. (2016). Evolving support vector machines using fruit fly optimization for medical data classification. Knowledge-Based Systems, 96, 61–75.

    Article  Google Scholar 

  • Shen, T., Jiang, J., Lin, W., Ge, J., Wu, P., Zhou, Y., Zuo, C., Wang, J., Yan, Z., & Shi, K. (2019). Use of overlapping group LASSO sparse deep belief network to discriminate Parkinson's disease and normal control. Frontiers in Neuroscience, 13, 396.

    Article  PubMed  PubMed Central  Google Scholar 

  • Shetty, S., & Rao, Y. (2016). SVM based machine learning approach to identify Parkinson’s disease using gait analysis. 2016 International Conference on Inventive Computation Technologies (ICICT). IEEE, 1-5.

  • Solana-Lavalle, G., & Rosas-Romero, R. (2021). Classification of PPMI MRI scans with voxel-based morphometry and machine learning to assist in the diagnosis of Parkinson’s disease. Computer Methods and Programs in Biomedicine, 198, 105793.

    Article  PubMed  Google Scholar 

  • Soltaninejad, S., Cheng, I., & BASU, A. (2018). Towards the identification of Parkinson’s disease using only T1 MR images. International conference on smart multimedia (pp. 145–156). Springer.

    Google Scholar 

  • Som, A., Krishnamurthi, N., Buman, M., & Turaga, P. (2020). Unsupervised Pre-trained Models from Healthy ADLs Improve Parkinson’s Disease Classification of Gait Patterns. 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 784-788.

  • Sujatha, J., & Rajagopalan, S. (2017). Performance evaluation of machine learning algorithms in the classification of Parkinson disease using voice attributes. International Journal of Applied Engineering Research, 12, 10669–10675.

    Google Scholar 

  • Suresh, P., & Vijayaraghavan, V. (2020). Gait recovery system for parkinson's disease using machine learning on embedded platforms. arXiv preprint arXiv:2004.05811.

  • Tahir, N. M., & Manap, H. H. (2012). Parkinson disease gait classification based on machine learning approach. Journal of Applied Sciences (Faisalabad), 12, 180–185.

    Article  Google Scholar 

  • Tian, Z.-Y., Qian, L., Fang, L., Peng, X.-H., Zhu, X.-H., Wu, M., Wang, W.-Z., Zhang, W.-H., Zhu, B.-Q., & Wan, M. (2020). Frequency-specific changes of resting brain activity in Parkinson’s disease: A machine learning approach. Neuroscience, 436, 170–183.

    Article  PubMed  Google Scholar 

  • Tsang, A. H., & Chung, K. K. (2009). Oxidative and nitrosative stress in Parkinson's disease. Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease, 1792, 643–650.

    Article  PubMed  Google Scholar 

  • Tuon, T., Valvassori, S., Dal Pont, G., Paganini, C., Pozzi, B., Luciano, T., Souza, P., Quevedo, J., Souza, C., & Pinho, R. (2014). Physical training prevents depressive symptoms and a decrease in brain-derived neurotrophic factor in Parkinson's disease. Brain Research Bulletin, 108, 106–112.

    Article  PubMed  Google Scholar 

  • Urcuqui, C., Castaño, Y., Delgado, J., Navarro, A., Diaz, J., Muñoz, B., & Orozco, J. (2018). Exploring Machine Learning to Analyze Parkinson's Disease Patients. 2018 14th International Conference on Semantics, Knowledge and Grids (SKG). IEEE, 160-166.

  • Wroge, T. J., Özkanca, Y., Demiroglu, C., Si, D., Atkins, D. C., & Ghomi, R. H. (2018). Parkinson’s disease diagnosis using machine learning and voice. 2018 IEEE Signal Processing in Medicine and Biology Symposium (SPMB). IEEE, 1-7.

  • Xu, S., & Pan, Z. (2020). A novel ensemble of random forest for assisting diagnosis of Parkinson's disease on small handwritten dynamics dataset. International Journal of Medical Informatics, 144, 104283.

    Article  PubMed  Google Scholar 

  • Xu, Z., Wang, J., Zhang, Y., & He, X. (2018). Voiceprint recognition of Parkinson patients based on deep learning. arXiv preprint arXiv:1812.06613.

  • Youngmann, B., Allerhand, L., Paltiel, O., Yom-Tov, E., & Arkadir, D. (2019). A machine learning algorithm successfully screens for Parkinson's in web users. Annals of Clinical Translational Neurology, 6, 2503–2509.

    Article  PubMed  PubMed Central  Google Scholar 

  • Zhang, J., Xu, C.-Y., & Liu, J. (2017a). Meta-analysis on the prevalence of REM sleep behavior disorder symptoms in Parkinson’s disease. BMC Neurology, 17, 1–6.

    Article  PubMed  PubMed Central  Google Scholar 

  • Zhang, K., Feng, B., Zhang, Y.-P., Huang, Y.-Z., & Wang, S.-Y. (2017b). Pattern identification of subthalamic local field potentials in Parkinson’s Disease. ITM Web of Conferences. EDP Sciences, 02004.

  • Zhang, J., Li, Y., Gao, Y., Hu, J., Huang, B., Rong, S., Chen, J., Zhang, Y., Wang, L., & Feng, S. (2020). An SBM-based machine learning model for identifying mild cognitive impairment in patients with Parkinson's disease. Journal of the Neurological Sciences, 418, 117077.

    Article  PubMed  Google Scholar 

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Acknowledgements

This review is as part of the research plan 4000126, funded by the student research committee of Kermanshah University of Medical Sciences.

Funding

By Deputy for Research and Technology, Kermanshah University of Medical Sciences (IR) (4000126). This deputy has no role in the study process.

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Authors and Affiliations

  1. Department of Biostatistics, School of Health, Kermanshah University of Medical Sciences, Kermanshah, Iran

    Nader Salari

  2. Student Research Committee, Kermanshah University of Medical Sciences, Kermanshah, Iran

    Mohsen Kazeminia

  3. audEERING GmbH, Gilching, Germany

    Hesam Sagha

  4. Research Centre for Computational Science and Mathematical Modelling, School of Computing, Electronics and Mathematics, Coventry University, Coventry, London, CV1 5FB, UK

    Alireza Daneshkhah

  5. Department of Electrical and Computer Engineering, Faculty of Engineering, University of Windsor, Windsor, Canada

    Arash Ahmadi

  6. Cellular and Molecular Research Center, Gerash University of Medical Sciences, Gerash, Iran

    Masoud Mohammadi

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  1. Nader Salari
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  2. Mohsen Kazeminia
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  3. Hesam Sagha
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  4. Alireza Daneshkhah
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  5. Arash Ahmadi
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Contributions

NS and MM and MK contributed to the design; MM participated in most of the study steps. MK and HS and AD prepared the manuscript. MM and NS and AH assisted in designing the study, and helped in the, interpretation of the study. All authors have read and approved the content of the manuscript.

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Correspondence to Masoud Mohammadi.

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Salari, N., Kazeminia, M., Sagha, H. et al. The performance of various machine learning methods for Parkinson’s disease recognition: a systematic review. Curr Psychol 42, 16637–16660 (2023). https://doi.org/10.1007/s12144-022-02949-8

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