A QUALITATIVE AND QUANTITATIVE APPROACH USING MACHINE LEARNING AND NON-MOTOR SYMPTOMS FOR PARKINSON’S DISEASE CLASSIFICATION. A HIERARCHICAL STUDY
Anitha Rani PALAKAYALA
anitha.palakayala@gmail.comVIT-AP University, School of Computer Science and Engineering (India)
https://orcid.org/0000-0001-7020-0284
Kuppusamy P
VIT-AP University, School of Computer Science and Engineering (India)
https://orcid.org/0000-0001-5369-8121
Abstract
Parkinson's Disease (PD) is a neurodegenerative disorder that impacts movement, speech, dexterity, and cognition. Clinical assessments primarily diagnose PD, but symptoms' variability often leads to misdiagnosis. This study examines ML algorithms to distinguish Healthy People (HP) from People with Parkinson's Disease (PPD). Data from 106 HP and 106 PPD participants, who underwent the Parkinson’s Disease Sleep Test (PDST), Hopkin’s Verbal Learning Test (HVLT), and Clock Drawing Test (CDT) from the Parkinson's Progression Markers Initiative (PPMI) were used. A custom HYBRID dataset was also created by integrating these 3 datasets. Various Machine Learning (ML) Classification Algorithms (CA) were also studied: Random Forest (RF), Naïve Bayes (NB), Support Vector Machine (SVM), and Logistic Regression (LR). Multiple feature sets: the first quartile (Q1: 25 % most important features), second quartile (Q2: 50 % most important features), third quartile (Q3: 75 % most important features), and fourth quartile (Q4: All 100 % features) were generated using various Feature Selection (FS) algorithms and ensemble mechanisms. Results showed that all the ML CA achieved over 73±8.4 % accuracy with individual datasets, while the proposed HYBRID dataset achieved a remarkable accuracy of 98±0.6 %. This study identified the optimal quantity of non-motor features, dataset, the best FS and CA in hierarchical approach for early PD diagnosis and also proved that PD may be diagnosed with great accuracy by analyzing non-motor PD parameters using ML algorithms. This suggests that extended data collection could serve as a digital biomarker for PD diagnosis in the future.
Keywords:
ensemble learning, machine learning, neurodegeneration, Parkinson's DiseaseReferences
Adeli, E., Shi, F., An, L., Wee, C.-Y., Wu, G., Wang, T., & Shen, D. (2016). Joint feature-sample selection and robust diagnosis of Parkinson’s disease from MRI data. NeuroImage, 141, 206-219. https://doi.org/10.1016/j.neuroimage.2016.05.054
DOI: https://doi.org/10.1016/j.neuroimage.2016.05.054
Google Scholar
Ali, L., Chakraborty, C., He, Z., Cao, W., Imrana, Y., & Rodrigues, J. J. P. C. (2022). A novel sample and feature dependent ensemble approach for Parkinson’s disease detection. Neural Computing and Applications, 35, 15997–16010. https://doi.org/10.1007/s00521-022-07046-2
DOI: https://doi.org/10.1007/s00521-022-07046-2
Google Scholar
Alkhatib, R., Diab, M. O., Corbier, C., & Badaoui. M. E. (2020). Machine Learning algorithm for gait analysis and classification on early detection of Parkinson. IEEE Sensors Letters, 4(6), 1-4. https://doi.org/10.1109/LSENS.2020.2994938
DOI: https://doi.org/10.1109/LSENS.2020.2994938
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(3), 195-202. https://doi.org/10.1016/j.artmed.2013.04.002
DOI: https://doi.org/10.1016/j.artmed.2013.04.002
Google Scholar
Benedict, R. H. B., Schretlen, D., Groninger, L., & Brandt, J. (1998). Hopkins verbal learning test - Revised: Normative data and analysis of inter-form and test-retest reliability. Clinical Neuropsychologist, 12(1), 43-55. https://doi.org/10.1076/clin.12.1.43.1726
DOI: https://doi.org/10.1076/clin.12.1.43.1726
Google Scholar
Breiman, L. (2001). Random forests. Machine Learning, 45, 5-32.
DOI: https://doi.org/10.1023/A:1010933404324
Google Scholar
Chaudhuri, K. R., Pal, S., DiMarco, A., Whately-Smith, S., Bridgman, K., Mathew, R., Pezzela, F. R., Forbes, A., Högl, B., & Trenkwalder, C. (2002). The Parkinson’s disease sleep scale: a new instrument for assessing sleep and nocturnal disability in Parkinson’s disease. J Neurol Neurosurg Psychiatry, 73(6), 629-635. https://doi.org/10.1136/jnnp.73.6.629
DOI: https://doi.org/10.1136/jnnp.73.6.629
Google Scholar
Connolly, B. S., & Lang, A. E. (2014). Pharmacological treatment of Parkinson disease: a review. JAMA, 311(16), 1670–1683. https://doi.org/10.1001/jama.2014.3654
DOI: https://doi.org/10.1001/jama.2014.3654
Google Scholar
Corani, G., & Benavoli, A. (2015). A bayesian approach for comparing cross-validated algorithms on multiple data sets. Machine Learning, 100, 285-304. https://doi.org/10.1007/s10994-015-5486-z
DOI: https://doi.org/10.1007/s10994-015-5486-z
Google Scholar
Cordella, F., Paffi, A., & Pallotti, A. (2021). Classification-based screening of Parkinson’s disease patients through voice signal. 2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA) (pp. 1-6). IEEE. https://doi.org/10.1109/MeMeA52024.2021.9478683
DOI: https://doi.org/10.1109/MeMeA52024.2021.9478683
Google Scholar
De Lau, L. M. L., & Breteler, M. M. B (2006). Epidemiology of Parkinson’s disease. The Lancet Neurology, 5(6), 525-535. https://doi.org/10.1016/S1474-4422(06)70471-9
DOI: https://doi.org/10.1016/S1474-4422(06)70471-9
Google Scholar
Drotár, P., Mekyska, J., Rektorová, I., Masarová, L., Smékal, Z., & Faundez-Zanuy, M. (2014). Decision support framework for Parkinson’s disease based on novel handwriting markers. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 23(3), 508-516. https://doi.org/10.1109/tnsre.2014.2359997
DOI: https://doi.org/10.1109/TNSRE.2014.2359997
Google Scholar
Govindu, A., & Palwe, S. (2023). Early detection of Parkinson's disease using machine learning. Procedia Computer Science, 218, 249-261. https://doi.org/10.1016/j.procs.2023.01.007
DOI: https://doi.org/10.1016/j.procs.2023.01.007
Google Scholar
Gunakala, A., & Shahid, A. H. (2023). A comparative study on performance of basic and ensemble classifiers with various datasets. Applied Computer Science, 19(1), 107-132. https://doi.org/10.35784/acs-2023-08
DOI: https://doi.org/10.35784/acs-2023-08
Google Scholar
Haq, A. U., Li, J. P., Memon, M. H., Khan, J., Malik, A., Ahmad, T., Ali, A., Nazir, S., Ahad, I., & Shahid, M. (2019). Feature selection based on L1-Norm support vector machine and effective recognition system for Parkinson’s Disease using voice recordings. IEEE Access, 7, 37718-37734. https://doi.org/10.1109/ACCESS.2019.2906350
DOI: https://doi.org/10.1109/ACCESS.2019.2906350
Google Scholar
Hosmer, D. W., Lemeshow, S. H., & Sturdivant, R. X. (2013). Applied Logistic Regression. John Wiley & Sons.
DOI: https://doi.org/10.1002/9781118548387
Google Scholar
Huang, F., Xu, H., Shen, T., & Jin, L. (2021). Recognition of Parkinson's Disease based on residual Neural Network and voice diagnosis. 2021 IEEE 5th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) (pp. 381-386). IEEE. http://dx.doi.org/10.1109/ITNEC52019.2021.9586915
DOI: https://doi.org/10.1109/ITNEC52019.2021.9586915
Google Scholar
Mabrouk, R., Chikhaoui, B., & Bentabet, L. (2018). Machine learning based classification using clinical and DaTSCAN SPECT imaging features: a study on Parkinson’s disease and SWEDD. IEEE Transactions on Radiation and Plasma Medical Sciences, 3(2), 170-177. https://doi.org/10.1109/TRPMS.2018.2877754
DOI: https://doi.org/10.1109/TRPMS.2018.2877754
Google Scholar
Mainland, B. J., & Shulman, K. I. (2017). Clock drawing test. In A. J. Larner (Ed.), Cognitive Screening Instruments (pp. 67–108). Springer International Publishing. https://doi.org/10.1007/978-3-319-44775-9_5
DOI: https://doi.org/10.1007/978-3-319-44775-9_5
Google Scholar
Martinez-Eguiluz, M., Arbelaitz, O., Gurrutxaga, I., Muguerza, J., Perona, I., Murueta-Goyena, A., Acera, M., Del Pino, R., Tijero, B., Gomez-Esteban, J. C., & Gabilondo, I. (2023). Diagnostic classification of Parkinson’s disease based on non-motor manifestations and machine learning strategies. Neural Computing and Applications, 35, 5603-5617. https://doi.org/10.1007/s00521-022-07256-8
DOI: https://doi.org/10.1007/s00521-022-07256-8
Google Scholar
Mei, J., Desrosiers, C., & Frasnelli, J. (2021). Machine Learning for the diagnosis of Parkinson's disease: A review of literature. Frontiers in Aging Neuroscience, 13, 633752. https://doi.org/10.3389/fnagi.2021.633752
DOI: https://doi.org/10.3389/fnagi.2021.633752
Google Scholar
Moradi, S., Tapak, L., & Afshar, S. (2022). Identification of novel non invasive diagnostics biomarkers in the Parkinson’s diseases and improving the disease classification using support vector machine. BioMed Research International, 2022(1), 009892. https://doi.org/10.1155/2022/5009892
DOI: https://doi.org/10.1155/2022/5009892
Google Scholar
Nuvoli, S., Spanu, A., Fravolini, M. L., Bianconi, F., Cascianelli, S., Madeddu, G., & Palumbo, B. (2020). [123i] Metaiodobenzylguanidine (MIBG) cardiac scintigraphy and automated classification techniques in Parkinsonian disorders. Molecular Imaging and Biology, 22(3), 703-710. https://doi.org/10.1007/s11307-019-01406-6
DOI: https://doi.org/10.1007/s11307-019-01406-6
Google Scholar
Pahwa, R., & Lyon, K. E. (2010). Early diagnosis of Parkinson’s disease: recommendations from diagnostic clinical guidelines. The American Journal Managed Care, 16, 94-99.
Google Scholar
Pisner, D. A., & Schnyer, D. M. (2020). Support vector machine. In: Machine Learning (pp. 101-121). Elsevier. http://dx.doi.org/10.1016/B978-0-12-815739-8.00006-7
DOI: https://doi.org/10.1016/B978-0-12-815739-8.00006-7
Google Scholar
Prashanth, R., Roy, S. D., Mandal, P. K., & Ghosh, S. (2014). Parkinson’s disease detection using olfactory loss and REM sleep disorder features. 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 5764-5767). IEEE. https://doi.org/10.1109/embc.2014.6944937
DOI: https://doi.org/10.1109/EMBC.2014.6944937
Google Scholar
Raundale, P., Thosar, C., & Rane, S. (2021). Prediction of Parkinson’s disease and severity of the disease using Machine Learning and Deep Learning algorithm. 2021 2nd International Conference for Emerging Technology (INCET) (pp. 1-5). IEEE. https://doi.org/10.1109/INCET51464.2021.9456292
DOI: https://doi.org/10.1109/INCET51464.2021.9456292
Google Scholar
Ricciardi, C., Amboni, M., De Santis, C., Ricciardelli, G., Improta, G., D’Addio, G., Cuoco, S., Picillo, M., Barone, P., & Cesarelli, M. (2020). Machine learning can detect the presence of mild cognitive impairment in patients affected by Parkinson’s disease. 2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA) (pp. 1-6). IEEE. https://doi.org/10.1109/MeMeA49120.2020.9137301
DOI: https://doi.org/10.1109/MeMeA49120.2020.9137301
Google Scholar
Sakar, B. E., Isenkul M. E., Sakar, C. O., Sertbas, A., Gurgen, F., Delil, S., Apaydin, H., & Kursun, O. (2013). Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings. IEEE J Biomed Health Informatics, 17(4), 828-834. https://doi.org/10.1109/jbhi.2013.2245674
DOI: https://doi.org/10.1109/JBHI.2013.2245674
Google Scholar
Schrag, A., Jahanshahi, M., & Quinn, N. (2000). How does Parkinson’s disease affect quality of life? A comparison with quality of life in the general population. Movement Disorders, 15(6), 1112-1118. https://doi.org/10.1002/1531-8257(200011)15:6%3C1112::aid-mds1008%3E3.0.co;2-a
DOI: https://doi.org/10.1002/1531-8257(200011)15:6<1112::AID-MDS1008>3.0.CO;2-A
Google Scholar
Smyth, C., Anjum, M. F., Ravi, S., Denison, T., Starr, P., & Little, S. (2023). Adaptive deep brain stimulation for sleep stage targeting in Parkinson’s disease. Brain Stimulation, 16(5), 1292-1296. https://doi.org/10.1016/j.brs.2023.08.006
DOI: https://doi.org/10.1016/j.brs.2023.08.006
Google Scholar
Thangaleela, S., Sivamaruthi, B. S., Kesika, P., Mariappan, S., Rashmi, S., Choeisoongnern, T., Sittiprapaporn, P., & Chaiyasut, C. (2023). Neurological insights into sleep disorders in Parkinson’s disease. Brain Sciences, 13(8), 1202. https://doi.org/10.3390/brainsci13081202
DOI: https://doi.org/10.3390/brainsci13081202
Google Scholar
Trenkwalder, C., Kohnen, R., Högl, B., Metta, V., Sixel-Döring, F., Frauscher, B., Hülsmann, J., Martinez-Martin, P., & Chaudhuri, K. R. (2011). Parkinson’s disease sleep scale-validation of the revised version PDSS-2. Movement Disorders, 26(4), 644-652. https://doi.org/10.1002/mds.23476
DOI: https://doi.org/10.1002/mds.23476
Google Scholar
Vellido, A. (2020). The importance of interpretability and visualization in machine learning for applications in medicine and health care. Neural Computing and Applications, 32, 18069-18083. https://doi.org/10.1007/s00521-019-04051-w
DOI: https://doi.org/10.1007/s00521-019-04051-w
Google Scholar
Wang, W., Lee, J., Harrou, F., & Sun, Y. (2020). Early detection of Parkinson’s disease using Deep Learning and Machine Learning. IEEE Access, 8, 147635-147646. https://doi.org/10.1109/ACCESS.2020.3016062
DOI: https://doi.org/10.1109/ACCESS.2020.3016062
Google Scholar
Wodzinski, M., Skalski, A., Hemmerling, D., Orozco-Arroyave, J. R., & Nöth, E. (2019). Deep Learning approach to Parkinson’s disease detection using voice recordings and convolutional Neural Network dedicated to image classification. 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 717-720). IEEE. https://doi.org/10.1109/EMBC.2019.8856972
DOI: https://doi.org/10.1109/EMBC.2019.8856972
Google Scholar
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) (pp. 1-7). IEEE. https://doi.org/10.1109/SPMB.2018.8615607
DOI: https://doi.org/10.1109/SPMB.2018.8615607
Google Scholar
Yu, T., & Zhu, H. (2020). Hyper-parameter optimization: a review of algorithms and applications. ArXiv, abs/2003.05689. https://doi.org/10.48550/arXiv.2003.05689
Google Scholar
Zhang, H. (2004). The optimality of naive bayes.The Florida AI Research Society.
Google Scholar
Authors
Anitha Rani PALAKAYALAanitha.palakayala@gmail.com
VIT-AP University, School of Computer Science and Engineering India
https://orcid.org/0000-0001-7020-0284
Authors
Kuppusamy PVIT-AP University, School of Computer Science and Engineering India
https://orcid.org/0000-0001-5369-8121
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