Abstract:
Liver cirrhosis is a highly infectious blood-borne illness that is often asymptomatic in its
early stages. Consequently, identifying and curing patients at an early stage of the ailment
is difficult. As the disease spreads to its advanced stages, diagnosis and treatment become
even more difficult. The goal of this research is to propose an artificial intelligence system
utilizing machine learning algorithms conservatively created to support physicians in
diagnosing liver cirrhosis at an early stage. To estimate the likelihood of a liver cirrhosis
infection, it was decided to construct a model for training produced machine learning
algorithms that use a variety of physiological variables. Three models for accurate
prognostication have been developed with different training, linking three different sets of
physiological variables and machine learning algorithms built on LR, SVM, DTC, GB and
RFC. The algorithm that performed the best in this challenge was Random Forest, which
had an accuracy of almost 86.74%. The approach was developed using the publiclyaccessible Liver Cirrhosis data source. The models used in this study had a significantly
higher accuracy than those used in previous studies, indicating their increased
dependability. Their resilience has been demonstrated by several model comparisons, and
the research study may be used to select the scheme.
