Liver Disease Prediction with SVM and Naive Bayes Algorithms

The objective of this research is to employ machine learning models to analyze medical data to forecast the risk of liver disease. To classify the risk level, Support Vector Machines (SVM) and Naïve Bayes algorithms analyze patient data, including bilirubin concentration, enzyme levels, and personal health measurements. The method generates precise, effective predictions by fusing SVM's capacity to handle high-dimensional data with the interpretability of Naïve Bayes. By providing information on patient risk levels, this application helps medical practitioners diagnose diseases early and take appropriate action. Predictive healthcare can be enhanced by integrating the lightweight model with health management systems.