Liver Cirrhosis Prediction Using Random Forest

Abstract: Liver cirrhosis, a chronic disease characterized by fibrosis and impaired liver function, poses significant diagnostic challenges. Early prediction is crucial for patient prognosis and timely intervention. This paper explores the applications of Random Forest, a robust ensemble learning technique, for predicting the stage of liver cirrhosis using a publicly available dataset. The process includes data cleaning, feature selection, model training, and performance analysis. The results show that Random Forest offers improved prediction accuracy compared to several traditional models, indicating its viability for real-world diagnostic use, outperforming several baseline models, thus validating its applicability in real-world clinical scenarios. 

Liver Cirrhosis, Random Forest, Machine Learning, Medical Diagnosis, Ensemble Learning, Feature Importance, Clinical Data, Non-Invasive Prediction

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