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Horizontal Voting Ensemble Based Predictive Modeling System for Colon Cancer

Ushaa Eswaran1,*, S. Anand2

1 Department of Electronics and Communication Engineering, Infant Jesus College of Engineering, Tuticorin, Tamil Nadu, India
2 Department of Computer Science and Engineering, Infant Jesus College of Engineering, Tuticorin, Tamil Nadu, India

* Corresponding Author: Ushaa Eswaran. Email:

Computer Systems Science and Engineering 2023, 46(2), 1917-1928.


Colon cancer is the third most commonly diagnosed cancer in the world. Most colon AdenoCArcinoma (ACA) arises from pre-existing benign polyps in the mucosa of the bowel. Thus, detecting benign at the earliest helps reduce the mortality rate. In this work, a Predictive Modeling System (PMS) is developed for the classification of colon cancer using the Horizontal Voting Ensemble (HVE) method. Identifying different patterns in microscopic images is essential to an effective classification system. A twelve-layer deep learning architecture has been developed to extract these patterns. The developed HVE algorithm can increase the system’s performance according to the combined models from the last epochs of the proposed architecture. Ten thousand (10000) microscopic images are taken to test the classification performance of the proposed PMS with the HVE method. The microscopic images obtained from the colon tissues are classified into ACA or benign by the proposed PMS. Results prove that the proposed PMS has ~8% performance improvement over the architecture without using the HVE method. The proposed PMS for colon cancer reduces the misclassification rate and attains 99.2% of sensitivity and 99.4% of specificity. The overall accuracy of the proposed PMS is 99.3%, and without using the HVE method, it is only 91.3%.


Cite This Article

U. Eswaran and S. Anand, "Horizontal voting ensemble based predictive modeling system for colon cancer," Computer Systems Science and Engineering, vol. 46, no.2, pp. 1917–1928, 2023.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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