The Applications of Machine Learning Algorithms in Multiple Sclerosis: A Systematic Review
Abstract
Multiple Sclerosis (MS) is a common chronic disease that affects society, especially young people. In recent years, data sciences have been used extensively to deal with the disease. Machine learning is one of the main data sciences types which has been used to deal with chronic diseases such as MS. This study aimed to identify the applications of machine learning algorithms in MS disease. This study is a systematic review that conducted in 2020. The searches were done in PubMed, Scopus, ISI Web of Sciences, Ovid, Science Direct, Embase, and Proquest scientific databases, by combining related keywords. Data extraction was done by using a data extraction form to follow the trends of this field of study. The results of the study showed that diagnosis of MS was the main application of machine learning in MS (33.3 %); also, assessment (24.24%) and prediction (18.18 %) of the disease were other main applications. The most used data type was medical images such as MRI and CT scans (55.17 %). The most used machine learning algorithm type was Support Vector Machine (SVM) (30 %) as a classification algorithm. The most optimized algorithm for the diagnosis and prediction of MS was KNN. It’s suggested to use machine learning algorithms to diagnose, assess, predict lesion classification, treatment, and severity determining of MS disease. Although the most common form of data used for MS is medical images, it is suggested that other types of data are generated to be used in machine learning algorithms. Considering the optimization rate of the algorithms used, it is suggested to pay more attention to the type of data and study objectives in data analysis using machine learning.
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15. Goldberg-Zimring D, Achiron A, Miron S, et al. Automated detection and characterization of multiple sclerosis lesions in brain MR images. Magn Reson Imaging. 1998;16(3):311-8. //doi.org/10.1016/S0730-725X(97)00300-7
16. Mortazavi D, Kouzani AZ, Soltanian-Zadeh H. Segmentation of multiple sclerosis lesions in MR images: a review. Neuroradiology. 2012;54(4):299-320. //doi.org/10.1007/s00234-011-0886-7
17. Xia Z, Secor E, Chibnik LB, et al. Modeling Disease Severity in Multiple Sclerosis Using Electronic Health Records. PloS one. 2013;8(11): e78927.//doi.org/10.1371/journal.pone.0078927
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20. Zhao Y, Chitnis T, Doan T. Ensemble learning for predicting multiple sclerosis disease course. Multiple Sclerosis Journal. 2019;25:160-1.
21. Zhao Y, Brodley CE, Chitnis T, et al. Addressing human subjectivity via transfer learning: An application to predicting disease outcome in multiple sclerosis patients. SIAM International Conference on Data Mining 2014, SDM 2014. //doi.org/10.1137/1.9781611973440.110
22. Zhang Y, Lu S, Zhou X, et al. Comparison of machine learning methods for stationary wavelet entropy-based multiple sclerosis detection: decision tree, k-nearest neighbors, and support vector machine. Simulation. 2016;92(9):861-71. //doi.org/10.1177/0037549716666962
23. Zhao Y, Chitnis T, Healy BC, et al. Domain induced dirichlet mixture of Gaussian processes: An application to predicting disease progression in multiple sclerosis patients. Proceedings - IEEE International Conference on Data Mining, ICDM; 2016. DOI: 10.1109/ICDM.2015.74
24. Yamamoto D, Arimura H, Kakeda S, et al. Computer-aided detection of multiple sclerosis lesions in brain magnetic resonance images: False positive reduction scheme consisted of rule-based, level set method, and support vector machine. Comput Med Imaging Graph. 2010;34(5):404-13. //doi.org/10.1016/j.compmedimag.2010.02.001
25. Yahia S, Ben Salem Y, Abdelkrim MN. Texture analysis of magnetic resonance brain images to assess multiple sclerosis lesions. Multimed Tools Appl. 2018;77(23):30769-89. //doi.org/10.1007/s11042-018-6160-9
26. Weygandt M, Hackmack K, Pfüller C, et al. MRI Pattern Recognition in Multiple Sclerosis Normal-Appearing Brain Areas. PloS one. 2011;6(6): e21138. ://doi.org/10.1371/journal.pone.0021138
27. Weiss N, Rueckert D, Rao A. Multiple sclerosis lesion segmentation using dictionary learning and sparse coding. Medical image computing and computer-assisted intervention: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention. 2013;16(Pt 1):735-42. //doi.org/10.1007/978-3-642-40811-3_92
28. Wang SH, Tang CS, Sun JD, et al. Multiple Sclerosis Identification by 14-Layer Convolutional Neural Network With Batch Normalization, Dropout, and Stochastic Pooling. Front Neurosci. 2018;12: 818. //doi.org/10.3389/fnins.2018.00818
29. Torabi A, Daliri MR, Sabzposhan SH. Diagnosis of multiple sclerosis from EEG signals using nonlinear methods. Australas Phys Eng Sci Med. 2017;40(4):785-97. //doi.org/10.1007/s13246-017-0584-9
30. Theocharakis P, Glotsos D, Kalatzis I, et al. Pattern recognition system for the discrimination of multiple sclerosis from cerebral microangiopathy lesions based on texture analysis of magnetic resonance images. Magn Reson Imaging. 2009;27(3):417-22. //doi.org/10.1016/j.mri.2008.07.014
31. Tardif CL, Collins DL, Eskildsen SF, et al. Segmentation of cortical MS lesions on MRI using automated laminar profile shape analysis. Medical image computing and computer-assisted intervention : MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention. 2010;13(Pt 3):181-8. //doi.org/10.1007/978-3-642-15711-0_23
32. Tadayon E, Khayati RM, Karami V, et al. A novel method for automatic classification of multiple sclerosis lesion subtypes using diffusion tensor MR images. Biomed Eng (Singapore). 2016;28(05):1650038. //doi.org/10.4015/S1016237216500381
33. Tacchella A, Romano S, Ferraldeschi M, et al. Collaboration between a human group and artificial intelligence can improve prediction of multiple sclerosis course: A proof-of-principle study. F1000Res. 2017;6: 2172. doi: 10.12688/f1000research.13114.2
34. Shahrbanian S, Duquette P, Kuspinar A, et al. Contribution of symptom clusters to multiple sclerosis consequences. Qual Life Res. 2015;24(3):617-29. //doi.org/10.1007/s11136-014-0804-7
35. Saccà V, Sarica A, Novellino F, et al. Evaluation of machine learning algorithms performance for the prediction of early multiple sclerosis from resting-state FMRI connectivity data. Brain Imaging Behav. 2019;13(4):1103-14. //doi.org/10.1007/s11682-018-9926-9
36. Kontschieder P, Dorn JF, Morrison C, et al. Quantifying progression of multiple sclerosis via classification of depth videos. Medical image computing and computer-assisted intervention : MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention. 2014;17(Pt 2):429-37. //doi.org/10.1007/978-3-319-10470-6_54
37. Jog A, Carass A, Pham DL, et al. Multi-output decision trees for lesion segmentation in multiple sclerosis: SPIE; 2015; 9413. //doi.org/10.1117/12.2082157
38. Elliott C, Francis SJ, Arnold DL, et al. Bayesian classification of multiple sclerosis lesions in longitudinal MRI using subtraction images. Medical image computing and computer-assisted intervention : MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention. 2010;13(Pt 2):290-7. //doi.org/10.1007/978-3-642-15745-5_36
39. Chitnis T, Zhao Y, Healy BC, et al. Predicting clinical course in multiple sclerosis using machine learning. InMULTIPLE SCLEROSIS JOURNAL 2014 Sep 1 (Vol. 20, pp. 404-404).
40. Chase HS, Mitrani LR, Lu GG, et al. Early recognition of multiple sclerosis using natural language processing of the electronic health record. BMC Med Inform Decis Mak. 2017;17(1):24. //doi.org/10.1186/s12911-017-0418-4
41. Bendfeldt K, Taschler B, Gaetano L, et al. MRI-based prediction of conversion from clinically isolated syndrome to clinically definite multiple sclerosis using SVM and lesion geometry. Brain Imaging Behav. 2019, 13(5):1361-1374. //doi.org/10.1007/s11682-018-9942-9
42. Kurbalija V, Ivanovic M, Budimac Z, et al. Multiple Sclerosis Diagnoses--Case-Base Reasoning Approach. InTwentieth IEEE International Symposium on Computer-Based Medical Systems (CBMS'07) 2007 Jun 20 (pp. 65-72). IEEE. DOI: 10.1109/CBMS.2007.75
43. Schwartz CE, Sprangers MA, Oort FJ, et al. Response shift in patients with multiple sclerosis: an application of three statistical techniques. Qual Life Res. 2011;20(10):1561-72. //doi.org/10.1007/s11136-011-0056-8
2. Lublin FD, Reingold SC, Cohen JA, et al. Defining the clinical course of multiple sclerosis: the 2013 revisions. Neurology. 2014;83(3):278-86. //doi.org/10.1212/WNL.0000000000000560
3. Alonso A, Jick SS, Olek MJ, et al. Incidence of multiple sclerosis in the United Kingdom. Journal of neurology. 2007 Dec 1;254(12):1736-41. doi: 10.1007/s00415-007-0602-z
4. Chataway J, Schuerer N, Alsanousi A, et al. Effect of high-dose simvastatin on brain atrophy and disability in secondary progressive multiple sclerosis (MS-STAT): a randomised, placebo-controlled, phase 2 trial. The Lancet. 2014;383(9936):2213-21. //doi.org/10.1016/S0140-6736(13)62242-4
5. Bronnum-Hansen H, Stenager E, Hansen T, Koch-Henriksen N. Survival and mortality rates among Danes with MS. Int MS J. 2006 May 1;13(2):66. PMID: 16635423
6. Raeisi Z, Ramezannezad P, Ahmadzade M, Tarahomi S. Analyzing clinical symptoms in multiple sclerosis using data mining. Tehran University Medical Journal TUMS Publications. 2017;75(1):39-48.
7. Leray E, Moreau T, Fromont A, et al. Epidemiology of multiple sclerosis. Revue neurologique. 2016 Jan 1;172(1):3-13. //doi.org/10.1016/j.neurol.2015.10.006
8. Phua C, Lee V, Smith K, et al. A comprehensive survey of data mining-based fraud detection research. arXiv preprint arXiv:1009.6119. 2010. DOI: 10.1016/j.chb.2012.01.002
9. Han J, Pei J, Kamber M. Data mining: concepts and techniques: Elsevier; 2011.
10. Abhari S, Niakan Kalhori SR, Ebrahimi M, et al. Artificial Intelligence Applications in Type 2 Diabetes Mellitus Care: Focus on Machine Learning Methods. J Healthc Inform Res. 2019; 25(4):248-61. doi.org/10.4258/hir.2019.25.4.248
11. Kantardzic M. Data mining: concepts, models, methods, and algorithms. John Wiley & Sons; 2011.
12. Yoo I, Alafaireet P, Marinov M, et al. Data mining in healthcare and biomedicine: a survey of the literature. J Med Syst. 2012 Aug 1;36(4):2431-48. //doi.org/10.1007/s10916-011-9710-5
13. Lukman S, He Y, Hui SC. Computational methods for traditional Chinese medicine: a survey. Comput Methods Programs Biomed. 2007 Dec 1;88(3):283-94. //doi.org/10.1016/j.cmpb.2007.09.008
14. Dagliati A, Marini S, Sacchi L, Cogni G, Teliti M, Tibollo V, et al. Machine learning methods to predict diabetes complications. J Diabetes Sci Technol. 2018; 12(2):295–302. //doi.org/10.1177/1932296817706375
15. Goldberg-Zimring D, Achiron A, Miron S, et al. Automated detection and characterization of multiple sclerosis lesions in brain MR images. Magn Reson Imaging. 1998;16(3):311-8. //doi.org/10.1016/S0730-725X(97)00300-7
16. Mortazavi D, Kouzani AZ, Soltanian-Zadeh H. Segmentation of multiple sclerosis lesions in MR images: a review. Neuroradiology. 2012;54(4):299-320. //doi.org/10.1007/s00234-011-0886-7
17. Xia Z, Secor E, Chibnik LB, et al. Modeling Disease Severity in Multiple Sclerosis Using Electronic Health Records. PloS one. 2013;8(11): e78927.//doi.org/10.1371/journal.pone.0078927
18. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71. //doi.org/10.1136/bmj.n71
19. Zhao YJ, Healy BC, Rotstein D, et al. Exploration of machine learning techniques in predicting multiple sclerosis disease course. PloS one. 2017;12(4). doi.org/10.1371/journal.pone.0174866
20. Zhao Y, Chitnis T, Doan T. Ensemble learning for predicting multiple sclerosis disease course. Multiple Sclerosis Journal. 2019;25:160-1.
21. Zhao Y, Brodley CE, Chitnis T, et al. Addressing human subjectivity via transfer learning: An application to predicting disease outcome in multiple sclerosis patients. SIAM International Conference on Data Mining 2014, SDM 2014. //doi.org/10.1137/1.9781611973440.110
22. Zhang Y, Lu S, Zhou X, et al. Comparison of machine learning methods for stationary wavelet entropy-based multiple sclerosis detection: decision tree, k-nearest neighbors, and support vector machine. Simulation. 2016;92(9):861-71. //doi.org/10.1177/0037549716666962
23. Zhao Y, Chitnis T, Healy BC, et al. Domain induced dirichlet mixture of Gaussian processes: An application to predicting disease progression in multiple sclerosis patients. Proceedings - IEEE International Conference on Data Mining, ICDM; 2016. DOI: 10.1109/ICDM.2015.74
24. Yamamoto D, Arimura H, Kakeda S, et al. Computer-aided detection of multiple sclerosis lesions in brain magnetic resonance images: False positive reduction scheme consisted of rule-based, level set method, and support vector machine. Comput Med Imaging Graph. 2010;34(5):404-13. //doi.org/10.1016/j.compmedimag.2010.02.001
25. Yahia S, Ben Salem Y, Abdelkrim MN. Texture analysis of magnetic resonance brain images to assess multiple sclerosis lesions. Multimed Tools Appl. 2018;77(23):30769-89. //doi.org/10.1007/s11042-018-6160-9
26. Weygandt M, Hackmack K, Pfüller C, et al. MRI Pattern Recognition in Multiple Sclerosis Normal-Appearing Brain Areas. PloS one. 2011;6(6): e21138. ://doi.org/10.1371/journal.pone.0021138
27. Weiss N, Rueckert D, Rao A. Multiple sclerosis lesion segmentation using dictionary learning and sparse coding. Medical image computing and computer-assisted intervention: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention. 2013;16(Pt 1):735-42. //doi.org/10.1007/978-3-642-40811-3_92
28. Wang SH, Tang CS, Sun JD, et al. Multiple Sclerosis Identification by 14-Layer Convolutional Neural Network With Batch Normalization, Dropout, and Stochastic Pooling. Front Neurosci. 2018;12: 818. //doi.org/10.3389/fnins.2018.00818
29. Torabi A, Daliri MR, Sabzposhan SH. Diagnosis of multiple sclerosis from EEG signals using nonlinear methods. Australas Phys Eng Sci Med. 2017;40(4):785-97. //doi.org/10.1007/s13246-017-0584-9
30. Theocharakis P, Glotsos D, Kalatzis I, et al. Pattern recognition system for the discrimination of multiple sclerosis from cerebral microangiopathy lesions based on texture analysis of magnetic resonance images. Magn Reson Imaging. 2009;27(3):417-22. //doi.org/10.1016/j.mri.2008.07.014
31. Tardif CL, Collins DL, Eskildsen SF, et al. Segmentation of cortical MS lesions on MRI using automated laminar profile shape analysis. Medical image computing and computer-assisted intervention : MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention. 2010;13(Pt 3):181-8. //doi.org/10.1007/978-3-642-15711-0_23
32. Tadayon E, Khayati RM, Karami V, et al. A novel method for automatic classification of multiple sclerosis lesion subtypes using diffusion tensor MR images. Biomed Eng (Singapore). 2016;28(05):1650038. //doi.org/10.4015/S1016237216500381
33. Tacchella A, Romano S, Ferraldeschi M, et al. Collaboration between a human group and artificial intelligence can improve prediction of multiple sclerosis course: A proof-of-principle study. F1000Res. 2017;6: 2172. doi: 10.12688/f1000research.13114.2
34. Shahrbanian S, Duquette P, Kuspinar A, et al. Contribution of symptom clusters to multiple sclerosis consequences. Qual Life Res. 2015;24(3):617-29. //doi.org/10.1007/s11136-014-0804-7
35. Saccà V, Sarica A, Novellino F, et al. Evaluation of machine learning algorithms performance for the prediction of early multiple sclerosis from resting-state FMRI connectivity data. Brain Imaging Behav. 2019;13(4):1103-14. //doi.org/10.1007/s11682-018-9926-9
36. Kontschieder P, Dorn JF, Morrison C, et al. Quantifying progression of multiple sclerosis via classification of depth videos. Medical image computing and computer-assisted intervention : MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention. 2014;17(Pt 2):429-37. //doi.org/10.1007/978-3-319-10470-6_54
37. Jog A, Carass A, Pham DL, et al. Multi-output decision trees for lesion segmentation in multiple sclerosis: SPIE; 2015; 9413. //doi.org/10.1117/12.2082157
38. Elliott C, Francis SJ, Arnold DL, et al. Bayesian classification of multiple sclerosis lesions in longitudinal MRI using subtraction images. Medical image computing and computer-assisted intervention : MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention. 2010;13(Pt 2):290-7. //doi.org/10.1007/978-3-642-15745-5_36
39. Chitnis T, Zhao Y, Healy BC, et al. Predicting clinical course in multiple sclerosis using machine learning. InMULTIPLE SCLEROSIS JOURNAL 2014 Sep 1 (Vol. 20, pp. 404-404).
40. Chase HS, Mitrani LR, Lu GG, et al. Early recognition of multiple sclerosis using natural language processing of the electronic health record. BMC Med Inform Decis Mak. 2017;17(1):24. //doi.org/10.1186/s12911-017-0418-4
41. Bendfeldt K, Taschler B, Gaetano L, et al. MRI-based prediction of conversion from clinically isolated syndrome to clinically definite multiple sclerosis using SVM and lesion geometry. Brain Imaging Behav. 2019, 13(5):1361-1374. //doi.org/10.1007/s11682-018-9942-9
42. Kurbalija V, Ivanovic M, Budimac Z, et al. Multiple Sclerosis Diagnoses--Case-Base Reasoning Approach. InTwentieth IEEE International Symposium on Computer-Based Medical Systems (CBMS'07) 2007 Jun 20 (pp. 65-72). IEEE. DOI: 10.1109/CBMS.2007.75
43. Schwartz CE, Sprangers MA, Oort FJ, et al. Response shift in patients with multiple sclerosis: an application of three statistical techniques. Qual Life Res. 2011;20(10):1561-72. //doi.org/10.1007/s11136-011-0056-8
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| Issue | Vol 60, No 5 (2022) | |
| Section | Review Article(s) | |
| DOI | https://doi.org/10.18502/acta.v60i5.9551 | |
| Keywords | ||
| Multiple sclerosis Machine learning Algorithm Classification | ||
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How to Cite
1.
Garavand A, Samadbeik M, Aslani N. The Applications of Machine Learning Algorithms in Multiple Sclerosis: A Systematic Review. Acta Med Iran. 2022;60(5):259-269.
