Random Forest Weighting based Feature Selection for C4.5 Algorithm on Wart Treatment Selection Method

Handoyo Widi Nugroho, Teguh Bharata Adji, Noor Akhmad Setiawan

Abstract


Research in the field of health, especially treatment of wart disease has been widely practiced. One of the research topics related to the treatment of wart disease is in order to provide the most appropriate treatment method recommendations. Treatment methods are widely used by doctors for treatment of patients with wart disease that is the method of cryotherapy and immunotherapy. Previous research has been done on  cryotherapy and immunotherapy datasets which resulted in two different prediction methods, but the accuracy level has not been satisfactory. In this study, two datasets are combined to produce a single prediction method. The method uses C4.5 algorithm combined with  Random Forest Feature Weighting (C4.5+RFFW) used to select the relevant features to improve accuracy. Experimental results show that the proposed method can improve performance with accuracy and informedness are 87.22% and 71.24%, respectively. These results further facilitate physicians in determining treatment methods for patients with a single predictive method and better-predicted performance.


Keywords


C4.5; Random Forest; Feature Weighting; Wart

Full Text:

PDF

References


L. Verma, S. Srivastava, and P. C. Negi, “A Hybrid Data Mining Model to Predict Coronary Artery Disease Cases Using Non-Invasive Clinical Data,” J. Med. Syst., vol. 40, no. 7, 2016.

C. S. Tucker, I. Behoora, H. B. Nembhard, M. Lewis, N. W. Sterling, and X. Huang, “Machine learning classification of medication adherence in patients with movement disorders using non-wearable sensors,” Comput. Biol. Med., vol. 66, pp. 120–134, 2015.

N. Memarian, S. Kim, S. Dewar, J. Engel, and R. J. Staba, “Multimodal data and machine learning for surgery outcome prediction in complicated cases of mesial temporal lobe epilepsy,” Comput. Biol. Med., vol. 64, pp. 67–78, 2015.

H. Mirzaalian, T. K. Lee, and G. Hamarneh, “Skin lesion tracking using structured graphical models,” Med. Image Anal., vol. 27, pp. 84–92, 2016.

V. K. Shrivastava, N. D. Londhe, R. S. Sonawane, and J. S. Suri, “Exploring the color feature power for psoriasis risk stratification and classification: A data mining paradigm,” Comput. Biol. Med., vol. 65, pp. 54–68, 2015.

D. M. Farid, L. Zhang, C. M. Rahman, M. A. Hossain, and R. Strachan, “Hybrid decision tree and naïve Bayes classifiers for multi-class classification tasks,” Expert Syst. Appl., vol. 41, no. 4 PART 2, pp. 1937–1946, 2014.

F. Khozeimeh, R. Alizadehsani, M. Roshanzamir, A. Khosravi, P. Layegh, and S. Nahavandi, “An expert system for selecting wart treatment method,” Comput. Biol. Med., vol. 81, no. August 2016, pp. 167–175, 2017.

F. Khozeimeh et al., “Intralesional immunotherapy compared to cryotherapy in the treatment of warts,” Int. J. Dermatol., vol. 56, no. 4, pp. 474–478, 2017.

C. Barbieri et al., “A new machine learning approach for predicting the response to anemia treatment in a large cohort of End Stage Renal Disease patients undergoing dialysis,” Comput. Biol. Med., vol. 61, pp. 56–61, 2015.

A. J. Masino, R. W. Grundmeier, J. W. Pennington, J. A. Germiller, and E. Bryan Crenshaw, “Temporal bone radiology report classification using open source machine learning and natural langue processing libraries,” BMC Med. Inform. Decis. Mak., vol. 16, no. 1, pp. 1–10, 2016.

J. Arevalo, F. A. González, R. Ramos-Pollán, J. L. Oliveira, and M. A. Guevara Lopez, “Representation learning for mammography mass lesion classification with convolutional neural networks,” Comput. Methods Programs Biomed., vol. 127, pp. 248–257, 2016.

C. Bergmeir and M. Ben, “frbs : Fuzzy Rule-Based Systems for Classification,” J. Stat. Softw., vol. 65, no. 6, pp. 1–30, 2015.

A. Wijaya and A. Bisri, “Hybrid decision tree and logistic regression classifier for email spam detection,” Proc. 2016 8th Int. Conf. Inf. Technol. Electr. Eng. Empower. Technol. Better Futur. ICITEE 2016, pp. 5–8, 2017.

J. Ross, Q. Morgan, and K. Publishers, “Book Review : C4 . 5 : Programs for Machine Learning,” vol. 240, pp. 235–240, 1994.

J. Ali, R. Khan, N. Ahmad, and I. Maqsood, “Random forests and decision trees,” IJCSI Int. J. Comput. Sci. Issues, vol. 9, no. 5, pp. 272–278, 2012.

E. Vigneau, P. Courcoux, R. Symoneaux, L. Guérin, and A. Villière, “Random forests: A machine learning methodology to highlight the volatile organic compounds involved in olfactory perception,” Food Qual. Prefer., vol. 68, no. February, pp. 135–145, 2018.

N. V. Chawla, “C4. 5 and imbalanced data sets: investigating the effect of sampling method, probabilistic estimate, and decision tree structure,” Proc. Int. Conf. Mach. Learn. Work. Learn. from Imbalanced Data Set II, 2003.

H. K. Sok, M. P. L. Ooi, Y. C. Kuang, and S. Demidenko, “Multivariate alternating decision trees,” Pattern Recognit., vol. 50, pp. 195–209, 2016.

J. R. Taylor, An Introduction to Error Analysis, Second. Clifornia: University Science Books, 1999.

D. M. W. Powers, “Evaluation: From Precision, Recall and F-Measure To Roc, Infor




DOI: http://dx.doi.org/10.18517/ijaseit.8.5.6504

Refbacks

  • There are currently no refbacks.



Published by INSIGHT - Indonesian Society for Knowledge and Human Development