Enhancing Liver Disease Prediction using Random Forest in Machine Learning Strategy

Liver diseases have become one of the most fatal disorders in several nations, with the number of affected patients increasing due to variables such as alcohol usage, inhalation of toxic gases, consumption of spoiled food, and using drugs. These disorders are now a major worldwide health concern, contributing significantly to morbidity and mortality rates. To address this expanding difficulty, liver patient databases were rigorously studied to create advanced classification models capable of reliably predicting liver illness. This study uses various liver disease risk identification techniques using machine learning according to the blood test results of an individual. Several classification techniques, such as Logistic Regression, J48, KNN, Random Forest classifiers, were implemented and rigorously evaluated using the Liver Patient dataset to see which one produced better predictive accuracies. Among these, the Random Forest Classifier emerged as the most accurate and reliable model, demonstrating superior performance in early disease detection. Our system allows users to input their blood test data, and the trained Random Forest model provides precise predictions regarding the risk of liver disease, thereby offering a valuable tool for early diagnosis, personalized healthcare, and timely medical intervention. By integrating advanced machine learning strategies, this approach enhances diagnostic accuracy and supports proactive health management.