ABSTRACT
Building a solid prediction model for the early identification of liver disorders is the major objective of this project. This approach will cover critical clinical criteria. The dataset is a complete collection of demographic and biological information acquired from diverse patient profiles. Finding deep relationships between genders is the collection's major purpose. Cross-validation approaches with accurate completion and logistic regression are performed to boost the prediction model's accuracy. The conclusions of this study supply the researchers with vital information on the many components that impact how accurate liver disease estimations are. By developing a broader grasp of the complicated processes involved, our research intends to stimulate the continuing development of diagnostic tools in the area of hepatology.
