Predicting Liver Disease Using Statistical Machine Learning Techniques

Abstract

Liver disease poses a significant global health challenge, with various etiologies and manifestations that can lead to severe complications if not promptly detected and managed. Early detection and precise prediction of liver disease are crucial for enhancing patient outcomes and reducing healthcare costs. This paper explores applying statistical machine learning techniques in predicting liver disease, focusing on analyzing relevant datasets and identifying critical features associated with the disease. It reviews a range of statistical machine-learning algorithms, including logistic regression, decision trees, support vector machines, and ensemble methods, assessing their efficacy in liver disease prediction. The paper also addresses potential predictive performance challenges like data quality, feature selection, and model interpretability. Additionally, it discusses future research directions and the broader applications of statistical machine learning in clinical settings, emphasizing its potential to revolutionize liver disease diagnostics and personalized treatment plans. By providing a comprehensive evaluation of statistical machine learning methods, this paper aims to inform and guide healthcare professionals and researchers in leveraging advanced analytics for improved liver disease prediction and management.