International Journal of Intelligent Systems and Applications(IJISA)
ISSN: 2074-904X (Print), ISSN: 2074-9058 (Online)
Published By: MECS Press
IJISA Vol.12, No.1, Feb. 2020
Wart Treatment Decision Support Using Support Vector Machine
Full Text (PDF, 955KB), PP.1-11
Warts are noncancerous benign tumors caused by the Human Papilloma Virus (HPV). The success rates of cryotherapy and immunotherapy, two common treatment methods for cutaneous warts, are 44% and 72%, respectively. The treatment methods, therefore, fail to cure a significant percentage of the patients. This study aims to develop a reliable machine learning model to accurately predict the success of immunotherapy and cryotherapy for individual patients based on their demographic and clinical characteristics. We employed support vector machine (SVM) classifier utilizing a dataset of 180 patients who were suffering from various types of warts and received treatment either by immunotherapy or cryotherapy. To balance the minority class, we utilized three different oversampling methods- synthetic minority oversampling technique (SMOTE), borderline-SMOTE, and adaptive synthetic (ADASYN) sampling. F-score along with sequential backward selection (SBS) algorithm were utilized to extract the best set of features. For the immunotherapy treatment method, SVM with radial basis function (RBF) kernel obtained an overall classification accuracy of 94.6% (sensitivity = 96.0%, specificity = 89.5%), and for the cryotherapy treatment method, SVM with polynomial kernel obtained an overall classification accuracy of 95.9% (sensitivity = 94.3%, specificity = 97.4%). The obtained results are competitive and comparable with the congeneric research works available in the literature, especially for the immunotherapy treatment method, we obtained 4.6% higher accuracy compared to the existing works. The developed methodology could potentially assist the dermatologists as a decision support tool by predicting the success of every unique patient before starting the treatment process.
Cite This Paper
Mamunur Rahman, Yuan Zhou, Shouyi Wang, Jamie Rogers, "Wart Treatment Decision Support Using Support Vector Machine", International Journal of Intelligent Systems and Applications(IJISA), Vol.12, No.1, pp.1-11, 2020. DOI: 10.5815/ijisa.2020.01.01
I. Ahmed, S. Agarwal, A. Ilchyshyn, S. Charles‐Holmes, J. Berth‐Jones, Liquid nitrogen cryotherapy of common warts: cryo‐spray vs. cotton wool bud, Br. J. Dermatol. 144 (2001) 1006–1009.
N. Tomson, J. Sterling, I. Ahmed, J. Hague, J. Berth‐Jones, Human papillomavirus typing of warts and response to cryotherapy, J. Eur. Acad. Dermatology Venereol. 25 (2011) 1108–1111.
S.C. Bruggink, J. Gussekloo, M.Y. Berger, K. Zaaijer, W.J.J. Assendelft, M.W.M. de Waal, J.N.B. Bavinck, B.W. Koes, J.A.H. Eekhof, Cryotherapy with liquid nitrogen versus topical salicylic acid application for cutaneous warts in primary care: randomized controlled trial, Can. Med. Assoc. J. 182 (2010) 1624–1630.
M. Maronn, C. Salm, V. Lyon, S. Galbraith, One‐year experience with candida antigen immunotherapy for warts and molluscum, Pediatr. Dermatol. 25 (2008) 189–192.
R.J. Signore, Candida albicans intralesional injection immunotherapy of warts, Cutis. 70 (2002) 185–192.
S.M. Johnson, P.K. Roberson, T.D. Horn, Intralesional injection of mumps or Candida skin test antigens: a novel immunotherapy for warts, Arch. Dermatol. 137 (2001) 451–455.
R.C. Phillips, T.S. Ruhl, J.L. Pfenninger, M.R. Garber, Treatment of warts with Candida antigen injection, Arch. Dermatology-Chicago. 136 (2000) 1274.
M.M. Rahman, S. Wang, Y. Zhou, J. Rogers, Predicting the Performance of Cryotherapy for Wart Treatment Using Machine Learning Algorithms, in: IISE Annu. Conf., Institute of Industrial and Systems Engineers, Orlando, FL, 2019.
E. Barati, M. Saraee, A. Mohammadi, N. Adibi, M.R. Ahmadzadeh, A survey on utilization of data mining approaches for dermatological (skin) diseases prediction, J. Sel. Areas Heal. Informatics. 2 (2011) 1–11.
S.O. Olatunji, H. Arif, Identification of Erythemato-Squamous skin diseases using extreme learning machine and artificial neural network, ICTACT J. Softw Comput. 4 (2013) 627–632.
A. Esteva, B. Kuprel, R.A. Novoa, J. Ko, S.M. Swetter, H.M. Blau, S. Thrun, Dermatologist-level classification of skin cancer with deep neural networks, Nature. 542 (2017) 115.
M. Antkowiak, Artificial Neural Networks vs. Support Vector machines for skin diseases recognition, Neural Networks. (2006).
F. Khozeimeh, R. Alizadehsani, M. Roshanzamir, A. Khosravi, P. Layegh, S. Nahavandi, An expert system for selecting wart treatment method, Comput. Biol. Med. 81 (2017) 167–175. doi:10.1016/j.compbiomed.2017.01.001.
M.A. Putra, N.A. Setiawan, S. Wibirama, Wart treatment method selection using AdaBoost with random forests as a weak learner, Commun. Sci. Technol. 3 (2018) 52–56.
S.B. Akben, Predicting the success of wart treatment methods using decision tree based fuzzy informative images, Biocybern. Biomed. Eng. 38 (2018) 819–827.
A.J. Guimarães, V.J.S. Araujo, P.V. de Campos Souza, V.S. Araujo, T.S. Rezende, Using Fuzzy Neural Networks to the Prediction of Improvement in Expert Systems for Treatment of Immunotherapy, in: Ibero-American Conf. Artif. Intell., Springer, 2018: pp. 229–240.
M. Abdar, V.N. Wijayaningrum, S. Hussain, R. Alizadehsani, P. Pławiak, U.R. Acharya, V. Makarenkov, IAPSO-AIRS: A Novel Improved Machine Learning-based System for Wart Disease Treatment, (n.d.).
D. Dheeru, E. Karra Taniskidou, UCI machine learning repository [https://archive.ics.uci.edu/ml/datasets/Cryotherapy+Dataset+], in: University of California, Irvine, School of Information and Computer Sciences, 2017.
D. Dheeru, E. Karra Taniskidou, UCI machine learning repository [https://archive.ics.uci.edu/ml/datasets/Immunotherapy+Dataset], in: University of California, Irvine, School of Information and Computer Sciences, 2017.
K. AKYOL, A. KARACI, Y. GÜLTEPE, A Study on Prediction Success of Machine Learning Algorithms for Wart Treatment, Int. Conf. Adv. Technol. Comput. Eng. Sci. (2018) 186–188.
L. Al Shalabi, Z. Shaaban, B. Kasasbeh, Data mining: A preprocessing engine, J. Comput. Sci. 2 (2006) 735–739.
M. Dash, H. Liu, Feature selection for classification, Intell. Data Anal. 1 (1997) 131–156.
L. Yu, H. Liu, Feature selection for high-dimensional data: A fast correlation-based filter solution, in: Proc. 20th Int. Conf. Mach. Learn., 2003: pp. 856–863.
S. Das, Filters, wrappers and a boosting-based hybrid for feature selection, in: Icml, 2001: pp. 74–81.
S. Maldonado, R. Weber, A wrapper method for feature selection using support vector machines, Inf. Sci. (Ny). 179 (2009) 2208–2217.
M.F. Akay, Support vector machines combined with feature selection for breast cancer diagnosis, Expert Syst. Appl. 36 (2009) 3240–3247. doi:10.1016/j.eswa.2008.01.009.
S. Güneş, K. Polat, Ş. Yosunkaya, Multi-class f-score feature selection approach to classification of obstructive sleep apnea syndrome, Expert Syst. Appl. 37 (2010) 998–1004.
M.M. Rahman, Y. Ghasemi, E. Suley, Y. Zhou, S. Wang, J. Rogers, Machine Learning Based Computer Aided Diagnosis of Breast Cancer Utilizing Anthropometric and Clinical Features.
Y.-W. Chen, C.-J. Lin, Combining SVMs with various feature selection strategies, in: Featur. Extr., Springer, 2006: pp. 315–324.
G.M. Weiss, F. Provost, Learning when training data are costly: The effect of class distribution on tree induction, J. Artif. Intell. Res. 19 (2003) 315–354.
Y. Sun, A.K.C. Wong, M.S. Kamel, Classification of imbalanced data: A review, Int. J. Pattern Recognit. Artif. Intell. 23 (2009) 687–719.
N. V Chawla, K.W. Bowyer, L.O. Hall, W.P. Kegelmeyer, SMOTE: synthetic minority over-sampling technique, J. Artif. Intell. Res. 16 (2002) 321–357.
H. Han, W.-Y. Wang, B.-H. Mao, Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning, in: Int. Conf. Intell. Comput., Springer, 2005: pp. 878–887.
H. He, Y. Bai, E.A. Garcia, S. Li, ADASYN: Adaptive synthetic sampling approach for imbalanced learning, in: 2008 IEEE Int. Jt. Conf. Neural Networks (IEEE World Congr. Comput. Intell., IEEE, 2008: pp. 1322–1328.
G. Lemaître, F. Nogueira, C.K. Aridas, Imbalanced-learn: A python toolbox to tackle the curse of imbalanced datasets in machine learning, J. Mach. Learn. Res. 18 (2017) 559–563.
S.J. Reeves, Z. Zhe, Sequential algorithms for observation selection, IEEE Trans. Signal Process. 47 (1999) 123–132.
V. Vapnik, The Nature of Statistical Learning Theory· 6·[MJ New York, Springer-Verlag. 1 (1995) 995.
K. Shankar, S.K. Lakshmanaprabu, D. Gupta, A. Maseleno, V.H.C. de Albuquerque, Optimal feature-based multi-kernel SVM approach for thyroid disease classification, J. Supercomput. (2018) 1–16.
G. Orru, W. Pettersson-Yeo, A.F. Marquand, G. Sartori, A. Mechelli, Using support vector machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review, Neurosci. Biobehav. Rev. 36 (2012) 1140–1152.
B. Magnin, L. Mesrob, S. Kinkingnéhun, M. Pélégrini-Issac, O. Colliot, M. Sarazin, B. Dubois, S. Lehéricy, H. Benali, Support vector machine-based classification of Alzheimer’s disease from whole-brain anatomical MRI, Neuroradiology. 51 (2009) 73–83.
S. Pan, S. Iplikci, K. Warwick, T.Z. Aziz, Parkinson’s Disease tremor classification–A comparison between Support Vector Machines and neural networks, Expert Syst. Appl. 39 (2012) 10764–10771.
B. Scholkopf, A.J. Smola, Learning with kernels: support vector machines, regularization, optimization, and beyond, MIT press, 2001.
C. Cortes, V. Vapnik, Support-vector networks, Mach. Learn. 20 (1995) 273–297.
B.E. Boser, I.M. Guyon, V.N. Vapnik, A training algorithm for optimal margin classifiers, in: Proc. Fifth Annu. Work. Comput. Learn. Theory, ACM, 1992: pp. 144–152.
A.G. Lalkhen, A. McCluskey, Clinical tests: sensitivity and specificity, Contin. Educ. Anaesth. Crit. Care Pain. 8 (2008) 221–223.
H.W. Nugroho, T.B. Adji, N.A. Setiawan, Random forest weighting based feature selection for c4. 5 algorithm on wart treatment selection method, Int. J. Adv. Sci. Eng. Inf. Technol. 8 (2018) 1858–1863.
R. Jain, R. Sawhney, P. Mathur, Feature Selection for Cryotherapy and Immunotherapy Treatment Methods Based on Gravitational Search Algorithm, in: 2018 Int. Conf. Curr. Trends Towar. Converging Technol., IEEE, 2018: pp. 1–7.
M.S. Basarslan, F. Kayaalp, A Hybrid Classification Example in the Diagnosis of Skin Disease with Cryotherapy and Immunotherapy Treatment, in: 2018 2nd Int. Symp. Multidiscip. Stud. Innov. Technol., IEEE, 2018: pp. 1–5.
A. Degirmenci, O. Karal, Evaluation of Kernel Effects on SVM Classification in the Success of Wart Treatment Methods, Am. J. Eng. Res. 7 (2018) 238–244.
A. Junio Guimarães, P. Vitor de Campos Souza, V. Jonathan Silva Araújo, T. Silva Rezende, V. Souza Araújo, Pruning Fuzzy Neural Network Applied to the Construction of Expert Systems to Aid in the Diagnosis of the Treatment of Cryotherapy and Immunotherapy, Big Data Cogn. Comput. 3 (2019) 22.
W. Jia, Y. Deng, C. Xin, X. Liu, W. Pedrycz, A classification algorithm with Linear Discriminant Analysis and Axiomatic Fuzzy Sets, Math. Found. Comput. 2 (2019) 73–81.