Revolutionizing Early Liver Disease Detection: Exploring Machine Learning and Ensemble Models

Authors

  • Rashmi Ashtagi Department of Computer Engineering, Vishwakarma Institute of Technology, Bibwewadi, Pune, Maharashtra, India
  • Vinayak Musale Department of Computer Engineering and Technology, Dr. Vishwanath Karad MIT World Peace University, Pune, India
  • Vaishali Sham Rajput Department of Computer Engineering, Vishwakarma Institute of Technology, Bibwewadi, Pune, Maharashtra, India
  • Sheela Chinchmalatpure Department of Computer Engineering, Vishwakarma Institute of Technology, Bibwewadi, Pune, Maharashtra, India
  • Sagar Mohite Department of Computer Engineering, Bharati Vidyapeeth Deemed University College of Engineering Pune, Maharashtra, India
  • Ranjeet Vasant Bidwe Symbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University) (SIU), Lavale, Pune, Maharashtra, India

Keywords:

Feature Selection, liver disease diagnostics, Predictive Modeling, Random Forest, Machine Learning Algorithms

Abstract

This study paper explores novel methods for early detection by integrating cutting-edge machine learning techniques in response to the growing global health issue caused by liver illnesses. By utilizing ensemble models like CatBoost, Gradient Boosting, and Random Forest, the research offers a thorough examination that goes beyond traditional diagnostic techniques. The ensemble model adds a new level of complexity and improves predictive accuracy in the prediction of cardiovascular disease by combining several different algorithms. The study carefully examines a wide range of datasets that include demographic, imaging, and clinical data, offering a comprehensive view of liver diseases. The Random Forest model that was selected turns out to be a remarkable performer; it is highly interpretable and provides important information about potential risk factors associated with liver disorders. The article highlights the possibilities for collaboration between data scientists and medical professionals and promotes the useful implementation of these models in decision support systems. Personalized risk assessments and prompt actions could result from this integration into clinical settings, which could lead to better patient outcomes. The study highlights the need for ongoing developments in predictive modeling to address the global burden of liver-related disorders and advances our understanding of the transformative influence of machine learning in liver disease diagnostics.

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References

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Published

29.01.2024

How to Cite

Ashtagi, R. ., Musale, V. ., Rajput, V. S. ., Chinchmalatpure, S. ., Mohite, S. ., & Bidwe, R. V. . (2024). Revolutionizing Early Liver Disease Detection: Exploring Machine Learning and Ensemble Models. International Journal of Intelligent Systems and Applications in Engineering, 12(13s), 528 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4619

Issue

Vol. 12 No. 13s (2024)

Section

Research Article

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