Wart treatment method selection using AdaBoost with random forests as a weak learner
Article Sidebar
Main Article Content
Abstract
Selection of wart treatment method using machine learning is being a concern to researchers. Machine learning is expected to select the treatment of warts such as cryotherapy and immunotherapy to patients appropriately. In this study, the data used were cryotherapy and immunotherapy datasets. This study aims to improve the accuracy of wart treatment selection with machine learning. Previously, there are several algorithms have been proposed which were able to provide good accuracy in this case. However, the existing results still need improvement to achieve better level of accuracy so that treatment selection can satisfy the patients. The purpose of this study is to increase the accuracy by improving the performance of weak learner algorithm of ensemble machine learning. AdaBoost is used in this study as a strong learner and Random Forest (RF) is used as a weak learner. Furthermore, stratified 10-fold cross validation is used to evaluate the proposed algorithm. The experimental results show accuracy of 96.6% and 91.1% in cryotherapy and immunotherapy respectively.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright
Open Access authors retain the copyrights of their papers, and all open access articles are distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided that the original work is properly cited.
The use of general descriptive names, trade names, trademarks, and so forth in this publication, even if not specifically identified, does not imply that these names are not protected by the relevant laws and regulations.
While the advice and information in this journal are believed to be true and accurate on the date of its going to press, neither the authors, the editors, nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein.
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
S. K. Loo and W. Y. Tang, Warts (non-genital), BMJ Clin. Evid. 2009 (2009) 1710.
S. Garg and S. Baveja, Intralesional immunotherapy for difficult to treat warts with Mycobacterium w vaccine, J. Cutan. Aesthet. Surg. 7 (2014) 203.
M. D. Lynch, J. Cliffe, and R. Morris-Jones, “Management of cutaneous viral warts, BMJ, 348 (2014) g3339.
S. Cockayne, et .al, Cryotherapy versus salicylic acid for the treatment of plantar warts (verrucae): a randomised controlled trial, BMJ 342 (2011) d3271.
D. McGeorge, Rupture of tendons after cryotherapy for hand warts, BMJ Br. Med. J. 297 (1988) 1475.
M. El-Khalawany, D. Shaaban and S. Aboeldahab, Immunotherapy of viral warts: myth and reality, Egypt. J. Dermatology Venerol. 35 (2015) 1-13.
F. Khozeimeh, R. Alizadehsani, M. Roshanzamir, A. Khosravi, P. Layegh, and S. Nahavandi, An expert system for selecting wart treatment method, Comput. Biol. Med. 81 (2017) 167–175.
F. Khozeimeh et al., Intralesional immunotherapy compared to cryotherapy in the treatment of warts, Int. J. Dermatol. 56 (2017) 474–478.
R. A. M Teimoorian, F Khozeimeh and P Layegh, Intralesional immunotherapy with Candida antigen compared to cryotherapy in the treatment of warts, in American Academy of Dermatology Conference, 2016.
H. W. Nugroho, T. B. Adji and 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.
S. B. Akben, Predicting the success of wart treatment methods using decision tree based fuzzy informative images, Biocybern. Biomed. Eng. 38 (2018) 819–827.
Y. Freund and R. E. Schapire, A decision-theoretic generalization of on-line learning and an application to boosting, J. Comput. Syst. Sci. 55 (1997) 119–139.
V. B. Vaghela, A. Ganatra and A. Thakkar, Boost a weak learner to a strong learner using ensemble system approach, in 2009 IEEE International Advance Computing Conference, IACC 2009, 2009, pp. 1432–1436.
L. Breiman, Random forest, Mach. Learn. 45 (2001) 5–32.
F. Khozeimeh, P. Layeghet, R. Alizadehsani and M. Roshanzamir, “Cryotherapy Dataset Data Set.” Internet: https://archive.ics.uci.edu/ml/datasets/Cryotherapy+Dataset+.
F. Khozeimeh, P. Layeghet, R. Alizadehsani and M. Roshanzamir, “Immunotherapy Dataset Data Set.” Internet: https://archive.ics.uci.edu/ml/datasets/Immunotherapy+Dataset.
A. Kraskov, H. Stögbauer and P. Grassberger, Estimating mutual information, Phys. Rev. E 69 (2004) 066138.
T. Huijskens, “Mutual information-based feature selection.” Internet: https://thuijskens.github.io/2017/10/07/feature-selection/.
G. Leshem and Y. Ritov, Traffic flow prediction using adaboost algorithm with random forests as a weak learner, Int. J. Math. Comput. Sci. 1 (2007) 193–198.
S. Bin and G. Sun, Data mining in census data with CART, in ICACTE 2010 - 2010 3rd International Conference on Advanced Computer Theory and Engineering, Proceedings vol 3, 2010.
R. A. Berk. Statistical Learning from a Regression Perspective - 2nd Edition, 2008, pp. 103-163.
A. W. Whitney, A direct method of nonparametric measurement selection, IEEE Trans. Comput. C–20 (1971) 1100–1103.
J. R. Vergara and P. A. Estévez, A review of feature selection methods based on mutual information, Neural Comput. Appl. 24 (2014) 175–186.
R. Kohavi, A study of cross-validation and bootstrap for accuracy estimation and model selection, Proc. 14th Int. Jt. Conf. Artif. Intell. - vol. 2, pp. 1137–1143, 1995.
W. J. Youden, Index for rating diagnostic tests, Cancer 3 (1950) 32–35.
F. Pedregosa, et. al, Scikit-learn: Machine learning in python, J. Mach. Learn. Res. 12 (2011) 2825-2830.