Classification of Liver Disease Using Conventional Tree-Based Machine Learning Approaches with Feature Prioritization Using a Heuristic Algorithm

Liver disease ranks as one of the leading causes of mortality globally, often going undetected until advanced stages. This study aims to enhance early detection of liver disease by employing machine learning models that utilize key health indicators. Utilizing the Indian Liver Patient Dataset (ILPD) from the UCI repository, we developed a predictive model using the CatBoost algorithm, achieving an initial accuracy of 74%. To improve this, feature selection was performed using the Whale Optimization Algorithm (WOA) and Harris Hawk Optimization (HHO), which increased accuracy to 82% and 85% respectively. The methodology involved preprocessing to correct data imbalances and outlier removal through univariate and bivariate analyses. These optimizations highlight the critical features enhancing the model's predictive capability. The results indicate that integrating metaheuristic algorithms in feature selection significantly improves the accuracy of liver disease prediction models. Future research could explore the integration of additional datasets and machine learning models to further refine predictive capabilities and understand the underlying pathophysiology of liver diseases.

@article{Mondal2024,
  title     = {Classification of Liver Disease Using Conventional Tree-Based Machine Learning Approaches with Feature Prioritization Using a Heuristic Algorithm},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {11},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Proloy Kumar Mondal and Haewon Byeon},
  doi       = {10.14569/IJACSA.2024.0151134},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151134}
}