Applying High-level Agreement Ensemble Classification Voting Techniques to Distinguish Inflammatory Bowel Disease

Document Type : Research Paper


1 Ph.D. Candidate, Department of Computer Engineering, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran

2 Assistant Prof, Department of Computer engineering, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran


Due to the complexity of medical decisions, there is a growing interest in the application of intelligence systems to support these decisions. In this paper, accordingly, the potential of several algorithms such as K Nearest Neighbor, Support Vector Machine, Random Forest, Naive Bayes, and Decision Tree was used to create an ensemble classification. Then, to obtain the voting result, high level agreement voting was used to evaluate the performance and make prediction. According to the involvement of body organs with this disease, the problem of diagnosing and differentiating various types of bowl inflammation was investigated. We should mention that higher prediction accuracy was obtained using the proposed model. The results and the comparisons of these methods showed that the proposed model indicates the highest prediction accuracy which is 98%. In the final step, the proposed model was evaluated applying the receiver operating characteristic curve model (ROC), and the area under the curve (AUC) was calculated.


Main Subjects

علی مردانی، س.؛ آقایی، ع. (1394). ارائۀ روش نظارتی برای نظرکاوی در زبان فارسی با استفاده از لغت‌نامه و الگوریتم SVM. مدیریت فناوری اطلاعات، 7(2)، 362- 345.
Ahmed, S. S., Dey, N., Ashour, A. S., Sifaki-Pistolla, D., Bălas-Timar, D., Balas, V. E., & Tavares, J. M. R. (2017). Effect of fuzzy partitioning in Crohn’s disease classification: a neuro-fuzzy-based approach. Medical & biological engineering & computing, 55(1), 101-115.
Alimardani, S., Aghaie, A. (2015). Opinion Mining in Persian Languageusing svm algorithm Journal of Information Technology Management, 7(2), 345-362. (in Persian)
Bashir, S., Qamar, U., Khan, F. H., & Naseem, L. (2016). HMV: A medical decision support framework using multi-layer classifiers for disease prediction. Journal of Computational Science, 13, 10-25.
Borut Sluban, A. & NadaLavrač, N. (2015). Relating ensemble diversity and performance: A study in class noise detection. Neurocomputing, 160, 120–131.
Catal, C., Alan, O. & Balkan, K. (2011). Class noise detection based on software metrics and ROC curves. Information Sciences, 181(21), 4867-4877.
Cooper, J.G.,  Purcell, G.P. (2006). Data Mining for Correlations between Diet and Crohn’s Disease Activity. AMIA Symposium Proceedings, Page – 897.
Guan, D., Yuan, W., & Shen, L. (2013, July). Class noise detection by multiple voting. IEEE. In Natural Computation (ICNC). Ninth International Conference on. pp. 906-911.
Guan, D., Yuan, W., Ma, T., & Lee, S. (2014). Detecting potential labeling errors for bioinformatics by multiple voting. Knowledge-Based Systems, 66, 28-35.
Kaladhar, D. S. V. G. K., Pottumuthu, B. K., Rao, P. V. N., Vadlamudi, V., Chaitanya, A. K., & Reddy, R. H. (1926). The Elements of Statistical Learning in Colon Cancer Datasets: Data Mining, Inference and Prediction. Algorithms Research, 2(1), 8-17.
Mossotto, E., Ashton, J.J., Coelho, T., Beattie, R.M., MacArthur, B.D., Ennis, S. (2017). Classification of Paediatric Inflammatory Bowel Disease using Machine Learning, 2017 May 25. doi: 10.1038/s41598-017-02606-2.
Olyaee, M. H., Yaghoubi, A., & Yaghoobi, M. (2016). Predicting protein structural classes based on complex networks and recurrence analysis. Journal of Theoretical Biology, 404, 375-382.
Sluban, B., & Lavrač, N. (2015). Relating ensemble diversity and performance: a study in class noise detection. Neurocomputing, 160, 120-131.  
Thompson, V. L. S., Lander, S., Xu, S., & Shyu, C. R. (2014). Identifying key variables in African American adherence to colorectal cancer screening: the application of data mining. BMC public health, 14(1), 1173.
Uğuz, H. (2011). Adaptive neuro-fuzzy inference system for diagnosis of the heart valve diseases using wavelet transform with entropy. Neural Computing and Applications, 21 (7), 1617-1628.