Efficient machine learning model to detect early stage Parkinson’s disease
Abstract
Parkinson’s disease (PD) often manifests itself in memory loss and cognitive decline. The decline is inexorable, and damage to the brain’s cortex has already occurred. Numerous studies have shown that by detecting dementia early and beginning treatment, the disease’s course can be slowed, and any further atrophy can be prevented. Brain imaging data, such as from an MRI, is frequently used in the diagnosis of Parkinson’s disease (PD). In recent years, utilizing deep convolutional neural networks has greatly improved Parkinson’s disease diagnosis. However, getting to the level of quality needed for clinical use is still challenging. In this study, we introduce a machine learning-based approach for more accurately diagnosing Parkinson’s disease. This research makes use of information gleaned from single-photon emission computerized tomography (SPECT) scan and positron emission tomography (PET) scans performed on patients with Parkinson’s disease (PD) and healthy controls. The most crucial characteristics of these datasets are isolated with the aid of the Fisher discriminate ratio (FDR) and non-negative matrix factorization (NMF). The K-nearest neighbor, Decision Tree, Support vector machine (SVM), and Deep Convolution neural network (DCCN) classifiers with confidence bounds classify the NMF-transformed data sets with a decreased number of features. The proposed DCCN technique has a classification accuracy of up to 93.7 percent when compared to decision trees, K-Nearest Neighbor (KNN)s, and SVMs. The DCCN is now a reliable approach for classifying SPECT and PET, PD images.
Keywords
Full Text:
PDFReferences
1. Hou Y, Shang H. Magnetic Resonance Imaging Markers for Cognitive Impairment in Parkinson’s Disease: Current View. Front Aging Neurosci. 2022; 14. doi: 10.3389/fnagi.2022.788846
2. Blair JC, Barrett MJ, Patrie J, et al. Brain MRI Reveals Ascending Atrophy in Parkinson’s Disease Across Severity. Front Neurol. 2019; 10. doi: 10.3389/fneur.2019.01329
3. Bron EE, Smits M, Niessen WJ, Klein S. Feature Selection Based on the SVM Weight Vector for Classification of Dementia. IEEE J Biomed Health Inform. 2015; 19(5): 1617-1626. doi: 10.1109/jbhi.2015.2432832
4. Aubin PM, Serackis A, Griskevicius J. Support Vector Machine Classification of Parkinson’s Disease, Essential Tremor and Healthy Control Subjects Based on Upper Extremity Motion. In: Proceedings of 2012 International Conference on Biomedical Engineering and Biotechnology; 2012. doi:10.1109/icbeb.2012.387
5. Zhang Y, Burock MA. Diffusion Tensor Imaging in Parkinson’s Disease and Parkinsonian Syndrome: A Systematic Review. Front Neurol. 2020; 11. doi:10.3389/fneur.2020.531993
6. Gupta D, Julka A, Jain S, et al. Optimized cuttlefish algorithm for diagnosis of Parkinson’s disease. Cognitive Systems Research. 2018; 52: 36-48. doi: 10.1016/j.cogsys.2018.06.006
7. Li T, Li W, Yang Y, Zhang W. Classification of brain disease in magnetic resonance images using two-stage local feature fusion. PLoS ONE. 2017; 12(2): e0171749. doi: 10.1371/journal.pone.0171749
8. Pandya S, Zeighami Y, Freeze B, et al. Predictive model of spread of Parkinson’s pathology using network diffusion. NeuroImage. 2019; 192: 178-194. doi: 10.1016/j.neuroimage.2019.03.001
9. Ramaniharan AK, Manoharan SC, Swaminathan R. Laplace Beltrami eigen value based classification of normal and Alzheimer MR images using parametric and non-parametric classifiers. Expert Systems with Applications. 2016; 59: 208-216. doi: 10.1016/j.eswa.2016.04.029
10. Rahman A, Rizvi SS, Khan A, Afzaal Abbasi A, Khan SU, Chung TS. Parkinson’s Disease Diagnosis in Cepstral Domain Using MFCC and Dimensionality Reduction with SVM Classifier. Mobile Information Systems. 2021; 2021: 1-10. doi: 10.1155/2021/8822069
11. Goldman JG, Litvan I. Mild cognitive impairment in Parkinson’s disease. Minerva Med. 2011; 102(6): 441-459.
12. Li S, Lei H, Zhou F, et al. Longitudinal and Multi-modal Data Learning for Parkinson’s Disease Diagnosis via Stacked Sparse Auto-encoder. 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). Published online April 2019. doi: 10.1109/isbi.2019.8759385
13. Davatzikos C, Fan Y, Wu X, Shen D, Resnick SM. Detection of prodromal Alzheimer’s disease via pattern classification of magnetic resonance imaging. Neurobiology of Aging. 2008; 29(4): 514-523. doi: 10.1016/j.neurobiolaging.2006.11.010
14. Prashanth R, Dutta Roy S, Mandal PK, Ghosh S. Automatic classification and prediction models for early Parkinson’s disease diagnosis from SPECT imaging. Expert Systems with Applications. 2014; 41(7): 3333-3342. doi: 10.1016/j.eswa.2013.11.031
15. Zhang J. Mining imaging and clinical data with machine learning approaches for the diagnosis and early detection of Parkinson’s disease. npj Parkinsons Dis. 2022; 8(1). doi: 10.1038/s41531-021-00266-8
16. Dickson DW. Parkinson’s Disease and Parkinsonism: Neuropathology. Cold Spring Harbor Perspectives in Medicine. 2012; 2(8): a009258-a009258. doi: 10.1101/cshperspect
17. Perez C, Roca YC, Naranjo L, Martin J. Diagnosis and Tracking of Parkinson’s Disease by using Automatically Extracted Acoustic Features. J Alzheimers Dis Parkinsonism. 2016; 6(5). doi: 10.4172/2161-0460.1000260
18. Gündüz H. Deep Learning-Based Parkinson’s Disease Classification Using Vocal Feature Sets. IEEE Access. 2019; 7: 115540-115551. doi: 10.1109/access.2019.2936564
19. Previtali F, Bertolazzi P, Felici G, Weitschek E. A novel method and software for automatically classifying Alzheimer’s disease patients by magnetic resonance imaging analysis. Computer Methods and Programs in Biomedicine. 2017; 143: 89-95. doi: 10.1016/j.cmpb.2017.03.006
20. Islam J, Zhang Y. A novel deep learning based multi-class classification method for Parkinson’s Disease detection using brain MRI data. In: Proceedings of International Conference on Brain Informatics; Beijing, China; 2017. pp. 213–222.
21. Lawton M, Ben-Shlomo Y, May MT, et al. Developing and validating Parkinson’s disease subtypes and their motor and cognitive progression. J Neurol Neurosurg Psychiatry. 2018; 89(12): 1279-1287. doi: 10.1136/jnnp-2018-318337
22. Jankovic J. Parkinson’s disease: Clinical features and diagnosis. Journal of Neurology, Neurosurgery & Psychiatry. 2008; 79(4): 368-376. doi: 10.1136/jnnp.2007.131045
23. Yadav R, Shukla G, Goyal V, Singh S, Behari M. A case control study of women with Parkinson’s disease and their fertility characteristics. Journal of the Neurological Sciences. 2012; 319(1-2): 135-138. doi: 10.1016/j.jns.2012.05.026
24. Rizzo G, Copetti M, Arcuti S, et al. Accuracy of clinical diagnosis of Parkinson disease. Neurology. 2016; 86(6): 566-576. doi: 10.1212/wnl.0000000000002350
DOI: https://doi.org/10.32629/jai.v7i3.1093
Refbacks
- There are currently no refbacks.
Copyright (c) 2023 Raziya Begum, T. Pavan Kumar
License URL: https://creativecommons.org/licenses/by-nc/4.0/