Ensemble Deep Support Vector Machine For Classification Of Liver Cirrhosis

Authors

  • Rajalakshmi S Asst Prof, Department of Computer Science and Engineering, Velammal Engineering College
  • Padma S Asst Prof, Department of Electronics and Instrumentation Engineering, Velammal Engineering College
  • K. C. Aarthi Asst Prof, Department of Computer Science and Engineering, Velammal Engineering College
  • P Rajeshwari Asst Prof, Department of Computer Science and Engineering, Velammal Engineering College

Keywords:

Ensemble DL, Liver Cirrhosis, Predictive Analytics, classification

Abstract

The escalating patient numbers, coupled with the high risk of progression to end-stage renal disease and inaccuracies in morbidity and mortality estimates, exert substantial pressure on healthcare infrastructure due to liver cirrhosis.The goal of this study is to create a machinelearning model that predicts population prevalence using comorbidity and pharmacological data. Predictive health care prediction using machine learning is a difficult task that can assist clinicians in determining the most effective therapies for saving lives. In this research, a machine learning algorithm, combined with ensemble learning techniques, is employed to estimate liver cirrhosis based on clinical data. These models are constructed using liver cirrhosis datasets, and their effectiveness is rigorously assessed to identify the most proficient liver cirrhosis classifier. This endeavor represents a significant stride in leveraging AI to enhance healthcare decision-making and mitigate the impact of liver cirrhosis on healthcare infrastructure.

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Published

2023-10-06

How to Cite

Rajalakshmi S, Padma S, K. C. Aarthi, & P Rajeshwari. (2023). Ensemble Deep Support Vector Machine For Classification Of Liver Cirrhosis. Chinese Journal of Computational Mechanics, (5), 281–287. Retrieved from http://jslxxb.cn/index.php/jslxxb/article/view/4360

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Articles