Comparison of machine learning methods for stationary wavelet entropy-based multiple sclerosis detection: Decision tree, k -nearest neighbors, and support vector machine

Simulation

Volumn 92, Issue 9, 2016, Pages 861-871

  Zhang, Yudong ^a,b   Lu, Siyuan ^a,c   Zhou, Xingxing ^a,d   Yang, Ming ^e   Wu, Lenan ^f   Liu, Bin ^g   Phillips, Preetha ^h   Wang, Shuihua ^a,i  

^a NANJING NORMAL UNIVERSITY   (China)

^b Hunan Policy Academy   (China)

^c ZHEJIANG UNIVERSITY   (China)

^d JILIN UNIVERSITY   (China)

^e NANJING MEDICAL UNIVERSITY   (China)

^f SOUTHEAST UNIVERSITY   (China)

^g AFFILIATED ZHONGDA HOSPITAL OF SOUTHEAST UNIVERSITY   (China)

^h SHEPHERD UNIVERSITY   (United States)

ⁱ City College of New York   (United States)

Author keywords

decision tree; k nearest neighbors; machine learning; Multiple sclerosis; stationary wavelet entropy; support vector machine

Indexed keywords

ARTIFICIAL INTELLIGENCE; BRAIN MAPPING; DECISION TREES; ENTROPY; IMAGE RETRIEVAL; MAGNETIC RESONANCE IMAGING; MOTION COMPENSATION; NEAREST NEIGHBOR SEARCH; SUPPORT VECTOR MACHINES;

CLASSIFICATION PERFORMANCE; K NEAREST NEIGHBOR (KNN); K-NEAREST NEIGHBORS; MACHINE LEARNING METHODS; MISCLASSIFICATION COSTS; MULTIPLE SCLEROSIS; STATE-OF-THE-ART APPROACH; STATIONARY WAVELET;

LEARNING SYSTEMS;

EID: 84988736270     PISSN: 00375497     EISSN: 17413133     Source Type: Journal    
DOI: 10.1177/0037549716666962     Document Type: Article

References (62)

1. Learmonth YC, Motl RW., Physical activity and exercise training in multiple sclerosis: a review and content analysis of qualitative research identifying perceived determinants and consequences. Disabil Rehabil 2016; 38: 1227-1242.
2. Bos SD, Berge T, Celius EG, et al. From genetic associations to functional studies in multiple sclerosis. Eur J Neurol 2016; 23: 847-853.
3. Raggi A, Covelli V, Schiavolin S, et al. Work-related problems in multiple sclerosis: a literature review on its associates and determinants. Disabil Rehabil 2016; 38: 936-944.
4. Sorensen PS, Sellebjerg F, Lycke J, et al. Minocycline added to subcutaneous interferon-1a in multiple sclerosis: randomized RECYCLINE study. Eur J Neurol 2016; 23: 861-870.
5. Tur C., Fatigue management in multiple sclerosis. Curr Treat Opt Neurol 2016; 18: Article ID 26.
6. Perrin PB, Panyavin I, Paredes AM, et al. A disproportionate burden of care: gender differences in mental health, health-related quality of life, and social support in Mexican multiple sclerosis caregivers. Behav Neurol 2015; 2015. DOI: 10.1155/2015/283958.
7. Costello F., Vision disturbances in multiple sclerosis. Semin Neurol 2016; 36: 185-195.
8. Kimbrough DJ, Sotirchos ES, Wilson JA, et al. Retinal damage and vision loss in African American multiple sclerosis patients. Ann Neurol 2015; 77: 228-236.
9. Longbrake EE, Lancia S, Tutlam N, et al. Impaired color vision as determined by Farnsworth Munsell 100 Hue testing is tightly associated with retinal thinning in multiple sclerosis. Multiple Sclerosis J 2014; 20: 358-358.
10. Hoang PD, Gandevia SC, Herbert RD., Prevalence of joint contractures and muscle weakness in people with multiple sclerosis. Disabil Rehabil 2014; 36: 1588-1593.
11. Burschka JM, Keune PM, Hofstadt-van Oy U, et al. Mindfulness-based interventions in multiple sclerosis: beneficial effects of Tai Chi on balance, coordination, fatigue and depression. BMC Neurol 2014; 14: Article ID 165.
12. Horvath A, Perlaki G, Toth A, et al. Increased diffusion in the normal appearing white matter of brain tumor patients: is this just tumor infiltration?. J Neuro Oncol 2016; 127: 83-90.
13. Magee R, Nolan YM, Downer EJ., An Assessment of inflammatory activity in human post-mortem cortical lesions and normal appearing white matter in multiple sclerosis. Ir J Med Sci 2016; 185: S19-S20.
14. Wegner C, Swiniarski A, Ott M, et al. Increased ongoing axonal injury in spinal normal-appearing white matter in multiple sclerosis as compared to neuromyelitis optica. Multiple Sclerosis J 2015; 21: 69-69.
15. Murray V, Rodriguez P, Pattichis MS., Multiscale AM-FM demodulation and image reconstruction methods with improved accuracy. IEEE Trans Image Proc 2010; 19: 1138-1152.
16. Siddiqui MF, Reza AW, Kanesan J., An automated and intelligent medical decision support system for brain MRI scans classification. Plos One 2015; 10: Article ID e0135875.
17. Phillips P, Dong Z, Yang J., Pathological brain detection in magnetic resonance imaging scanning by wavelet entropy and hybridization of biogeography-based optimization and particle swarm optimization. Progr Electromagn Res 2015; 152: 41-58.
18. Khotanlou H, Afrasiabi M., Feature selection in order to extract multiple sclerosis lesions automatically in 3D brain magnetic resonance images using combination of support vector machine and genetic algorithm. J Med Signal Sens 2012; 2: 211-218.
19. Tachinaga S, Hiura Y, Kawashita I, et al. Development of a computer-aided diagnostic system for detecting multiple sclerosis using magnetic resonance images. Nihon Hoshasen Gijutsu Gakkai zasshi 2014; 70: 223-229.
20. Deshpande H, Maurel P, Barillot C., Classification of multiple sclerosis lesions using adaptive dictionary learning. Comput Med Imag Graph 2015; 46: 2-10.
21. Nayak DR, Dash R, Majhi B., Brain MR image classification using two-dimensional discrete wavelet transform and AdaBoost with random forests. Neurocomputing 2016; 177: 188-197.
22. Saritha M, Joseph KP, Mathew AT., Classification of MRI brain images using combined wavelet entropy based spider web plots and probabilistic neural network. Pattern Recognit Lett 2013; 34: 2151-2156.
23. Zhou XX, Ji GL, Yang JQ, et al. Detection of abnormal MR brains based on wavelet entropy and feature selection. IEEJ Trans Electr Electr Eng 2016; 11: 364-373.
24. Du S, Atangana A, Liu A, et al. Application of stationary wavelet entropy in pathological brain detection. Multimedia Tool Appl. Epub ahead of print 11 March 2016. DOI: 10.1007/s11042-016-3401-7.
25. Negi SS, Bhandari YS,. A hybrid approach to image enhancement using contrast stretching on image sharpening and the analysis of various cases arising using histogram. In: recent advances and innovations in engineering (ICRAIE), Jaipur, 2014.
26. Langarizadeh M, Mahmud R, Ramli AR, et al. Improvement of digital mammogram images using histogram equalization, histogram stretching and median filter. J Med Eng Technol 2011; 35: 103-108.
27. Nguyen N, Vo A, Choi I, et al. A stationary wavelet entropy-based clustering approach accurately predicts gene expression. J Comput Biol 2015; 22: 236-249.
28. Lenis G, Pilia N, Oesterlein T, et al. P wave detection and delineation in the ECG based on the phase free stationary wavelet transform and using intracardiac atrial electrograms as reference. Biomed Eng Biomedizinische Technik 2016; 61: 37-56.
29. Merah M, Abdelmalik TA, Larbi BH., R-peaks detection based on stationary wavelet transform. Comput Meth Program Biomed 2015; 121: 149-160.
30. Dong Z, Liu AJ, Wang S, et al. Magnetic resonance brain image classification via stationary wavelet transform and generalized eigenvalue proximal support vector machine. J Med Imag Health Inform 2015; 5: 1395-1403.
31. Langerudi MF, Rashidi TH, Mohammadian A., Individual trip rate transferability analysis based on a decision tree approach. Transp Plann Technol 2016; 39: 370-388.
32. Bernauer F, Hurkamp K, Ruhm W, et al. Snow event classification with a 2D video disdrometer-a decision tree approach. Atmosph Res 2016; 172: 186-195.
33. Zhang Y, Wang S, Phillips P, et al.Binary PSO with mutation operator for feature selection using decision tree applied to spam detection. Knowl Base Syst 2014; 64: 22-31.
34. Magniez F, Nayak A, Santha M, et al. Improved bounds for the randomized decision tree complexity of recursive majority. Random Struct Algorithm 2016; 48: 612-638.
35. Wylie CE, Shaw DJ, Verheyen KLP, et al. Decision-tree analysis of clinical data to aid diagnostic reasoning for equine laminitis: a cross-sectional study. Vet Rec 2016; 178: 8.
36. Sathyadevan S, Nair RR., Comparative analysis of decision tree algorithms: ID3, C4.5 and random forest. In: Jain LC, Behera HS, Mandal JK, et al. Computational intelligence in data mining. Vol. 31 of Smart Innovation Systems and Technologies. Berlin: Springer, 2015, pp. 549-562.
37. An FW, Mihara K, Yamasaki S, et al. Highly flexible nearest-neighbor-search associative memory with integrated k nearest neighbor classifier, configurable parallelism and dual-storage space. Jpn J Appl Phys 2016; 55: Article ID 04ef10.
38. Chatzimilioudis G, Costa C, Zeinalipour-Yazti D, et al. Distributed in-memory processing of all k nearest neighbor queries. IEEE Trans Knowl Data Eng 2016; 28: 925-938.
39. Park Y, Hwang H, Lee SG., A novel algorithm for scalable k-nearest neighbour graph construction. J Inform Sci 2016; 42: 274-288.
40. Chon AT., Design of lazy classifier based on fuzzy k-nearest neighbors and reconstruction error. J Korean Inst Intell Syst 2010; 20: 101-108.
41. Gonzalez M, Bergmeir C, Triguero I, et al. On the stopping criteria for k-nearest neighbor in positive unlabeled time series classification problems. Inform Sci 2016; 328: 42-59.
42. Siriteerakul T, Boonjing V, Gullayanon R., Character classification framework based on support vector machine and k-nearest neighbour schemes. Scienceasia 2016; 42: 46-51.
43. Almasi ON, Rouhani M,. Fast and de-noise support vector machine training method based on fuzzy clustering method for large real world datasets. Turk J Electr Eng Comput Sci 2016; 24: 219-233.
44. Yang X-J, Dong Z-C, Liu G, et al. Pathological brain detection in MRI scanning by wavelet packet Tsallis entropy and fuzzy support vector machine. SpringerPlus 2015; 4: Article ID 716.
45. Carrasco M, Lopez J, Maldonado S., A second-order cone programming formulation for nonparallel hyperplane support vector machine. Exp Syst Appl 2016; 54: 95-104.
46. Iosifidis A, Gabbouj M., Multi-class support vector machine classifiers using intrinsic and penalty graphs. Pattern Recognit 2016; 55: 231-246.
47. Wang S, Yang X, Zhang Y, et al. Identification of green, Oolong and black teas in China via wavelet packet entropy and fuzzy support vector machine. Entropy 2015; 17: 6663-6682.
48. Negri RG, Dutra LV, Sant'Anna SJS,. Comparing support vector machine contextual approaches for urban area classification. Remote Sens Lett 2016; 7: 485-494.
49. Zhang Y-D, Chen S, Wang S-H, et al. Magnetic resonance brain image classification based on weighted-type fractional Fourier transform and nonparallel support vector machine. Int J Imag Syst Technol 2015; 25: 317-327.
50. Newby D, Freitas AA, Ghafourian T., Coping with unbalanced class data sets in oral absorption models. J Chem Inform Model 2013; 53: 461-474.
51. Thomasian A, Li Y, Zhang LJ., Optimal subspace dimensionality for k-nearest-neighbor queries on clustered and dimensionality reduced datasets with SVD. Multimedia Tool Appl 2008; 40: 241-259.
52. Porta A, Castiglioni P, Bari V, et al. K-nearest-neighbor conditional entropy approach for the assessment of the short-term complexity of cardiovascular control. Physiol Meas 2013; 34: 17-33.
53. Lopez FJM, Puertas SM, Arriaza JAT., Training of support vector machine with the use of multivariate normalization. Appl Soft Comput 2014; 24: 1105-1111.
54. Chavan S, Abdelaziz A, Wiklander JG, et al. A k-nearest neighbor classification of hERG K+ channel blockers. J Comput Aid Mol Des 2016; 30: 229-236.
55. Tanveer M, Shubham K, Aldhaifallah M, et al. An efficient regularized k-nearest neighbor based weighted twin support vector regression. Knowl Base Syst 2016; 94: 70-87.
56. Wu L., Pattern recognition via PCNN and Tsallis entropy. Sensors 2008; 8: 7518-7529.
57. Stosic D, Stosic D, Ludermir T, et al. Correlations of multiscale entropy in the FX market. Phys A Stat Mech Appl 2016; 457: 52-61.
58. Sajjadi S, Shamshirband S, Alizamir M, et al. Extreme learning machine for prediction of heat load in district heating systems. Energ Build 2016; 122: 222-227.
59. Zhou X-X, Yang J-F, Sheng H, et al. Combination of stationary wavelet transform and kernel support vector machines for pathological brain detection. Simulation. Epub ahead of print 2 March 2016. DOI: 10.1177/0037549716629227.
60. Zhang Y, Wu L, Naggaz N, et al. Remote-sensing image classification based on an improved probabilistic neural network. Sensors 2009; 9: 7516-7539.
61. Audhkhasi K, Osoba O, Kosko B., Noise-enhanced convolutional neural networks. Neural Network 2016; 78: 15-23.
62. Zhao JL, Huang WJ,. Integrating Landsat TM imagery and see5 decision-tree software for identifying croplands: a case study in Shunyi District, Beijing. In: Gong Z, Luo XF, Chen JJ, et al. (eds) Web information systems and mining. (Lecture Notes in Computer Science, Vol. 6987). Berlin: Springer Press, 2011, pp. 251-258.