Accessing Liver Disease Severity Levels from Electronic Health Records Using a Kernel-Driven Meta-Heuristic Approach

Abstract

Liver diseases are one of the major health burdens globally, affecting millions each year, with an increasing need for timely and accurate stratification of patients into various care pathways to optimize both outcomes and resources. This work uses machine learning techniques in the development of a robust model to classify liver disease patients as either inpatients or outpatients using data extracted from EHRs. The major steps involved in the process are normalization of data for feature consistency and a PCA-driven feature selection process for computational efficiency. Among the different models compared, KELM performed the best on all metrics of accuracy, precision, recall, and F1-score, closely followed by KFDA. These results emphasize the impact of preprocessing and dimensionality reduction in enhancing kernel-based algorithms and demonstrate the role of ML in clinical decision support. The approach developed is scalable, interpretable, and effective for the triage of liver disease patients and will contribute to better resource utilization and improved patient outcomes in clinical settings.