Forecasting Liver Disorders with Machine Learning Models

Abstract

Liver disorders encompass a spectrum of ailments that impact the liver, a crucial organ responsible for a variety of vital bodily functions. These functions encompass metabolic processes, detoxification, protein synthesis, and the production of bile. Liver maladies can arise from various sources, such as viral infections (e.g., hepatitis), excessive alcohol consumption, conditions related to obesity (like non-alcoholic fatty liver disease), autoimmune conditions, genetic predisposition, or exposure to toxins. Common signs and symptoms may encompass fatigue, jaundice, abdominal discomfort, and digestive problems. In our study, we gather data and employ five distinct machine learning classification algorithms: Random Forest, Decision Tree, Naïve Bayes, K-Nearest Neighbor, and XG Boost. After constructing models and evaluating their performance, we observed that XG Boost achieved an impressive accuracy rate of 99.8%.