A DIAGNOSTIC MODEL FOR THE PREDICTION OF LIVER CIRRHOSIS USING MACHINE LEARNING TECHNIQUES

Liver cirrhosis is the most common type of chronic liver disease in globe. The ability to forecast onset sickness critical for successful treatment and prevention catastrophic health implications. As a result, researchers created prediction model using machine learning techniques. This study was based on dataset from Federal Medical Centre, Yola, which included 583 patient instances 11 attributes. proposed employed Nave Bayes, Classification Regression Tree (CART), Support Vector Machine (SVM) with 10-fold cross-validation. Accuracy, precision, recall, F1 Score were used evaluate model's performance. Among all strategies this study, technique produces best results, accuracy 73%, precision recall 100%, 84%. Based medical data FMC, shows that methods, specifically Machine, provide more accurate sickness. approach can be help doctors make better clinical decisions.