Machine Learning Strategies for Classification of Liver Cirrhosis

Liver cirrhosis is a difficult, long-term disease that requires prompt and accurate diagnosis to give patients the best treatment possible. However, the data involved in diagnosing cirrhosis is often complex and varied, which can make traditional diagnostic approaches less dependable. This can sometimes lead to delays in forming treatment plans and affect essential clinical decisions. This research seeks to overcome these challenges by applying machine learning, specifically using decision trees and random forest algorithms, to improve how cirrhosis stages are classified. These algorithms are particularly effective for handling complex datasets and produce results that are both clear and interpretable, allowing healthcare providers to make more informed decisions. By boosting classification accuracy, the proposed model aims to become a reliable tool in diagnosing cirrhosis, supporting healthcare professionals in developing personalized treatment plans tailored to the unique needs of each patient.