Title: Survivability prediction of patients suffering hepatocellular carcinoma using diverse classifier ensemble of grafted decision tree

Authors: Ranjit Panigrahi; Moumita Pramanik; Udit Kumar Chakraborty; Akash Kumar Bhoi

Addresses: Department of Computer Applications, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Gangtok, Sikkim, India ' Department of Computer Applications, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Gangtok, Sikkim, India ' Department of Computer Science and Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Gangtok, Sikkim, India ' Department of Electrical and Electronics Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Gangtok, Sikkim, India

Abstract: The mortality rate of patients who have cancer is the second highest cause of death around the globe. Hepatocellular Carcinoma (HCC), a type of liver cancer, is once such a cause of death. Though the probability of survival of patients is very rare, a mechanism to predict chances of survivability will provide a great aid to the medical practitioners to treat patients suffering from HCC. In this article, two state of the art survivability prediction schemes have been proposed separately for male and female subjects suffering HCC. The prediction engine employs Feature Selection Via (FSV) concave minimisation feature ranking and Sigmis feature selection scheme to extract limited features of both male and female subjects and an ensemble of decision tree grafting mechanism successfully predicts the chances of survivability of HCC patients. The gender-specific survivability prediction engine is the first-ever such prediction model for the diagnosis of HCC.

Keywords: hepatocellular carcinoma; HCC; liver cancer detection; Sigmis; FSV; machine learning.

DOI: 10.1504/IJCAT.2020.10034781

International Journal of Computer Applications in Technology, 2020 Vol.64 No.4, pp.349 - 360

Received: 05 Apr 2020
Accepted: 16 May 2020

Published online: 28 Jan 2021 *

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