As seen from the previous literature overview various ML algorithms have been utilized to detect hepatitis C patients with relatively high classification accuracy. The problem that arises from the majority of utilized ML models is the inability to transform these models into simple mathematical equations which could be easily used for the detection of Hepatitis C patients with high classification accuracy. Generally, mathematical equations require fewer computational resources when compared to the entire ML models. The novelty of this research is to show how to apply the genetic programming-symbolic classifier (GPSC) on a publicly available dataset [19] to obtain symbolic expressions which could detect the Hepatitis C patients and/or to detect the stage of Hepatitis C (hepatitis C/Fibrosis/cirrhosis) with high classification accuracy. Due to a large imbalance between class samples in the original dataset (a large number of healthy patients and a small number of Hepatitis C, Fibrosis), the novelty is to show how using dataset balancing methods can balance the dataset and in the end influence the classification accuracy of obtained symbolic expression. Since the original dataset is highly imbalanced only balanced variations of the original dataset will be used in this research and the best symbolic expressions will be evaluated on the original dataset. To summarize the novelty of this paper is to show the procedure of how symbolic expressions can be obtained using the GPSC algorithm and unbalanced dataset for the detection of Hepatitis C patients us ing the parameters (enzymes, proteins, and biomarker values) of their blood samples as input variables. The GPSC is a variant of GP alongside symbolic regression and it is a method that evolves the randomly generated initial population that is not fit for solving a particular problem and making them fit with the application of crossover and mutation from generation to generation. Generally, GP is classified as an evolutionary algorithm, however, the process of obtaining the symbolic expression is similar to the supervised learning method i.e., the GP requires a dataset with defined input variables and targeted output variable from which symbolic expression is obtained. From the previous literature overview and the idea/novelty of this paper the following questions arise:  
* is it possible to obtain symbolic expressions that can detect Hepatitis C and the progress of the disease (Hepatitis C/fibrosis/cirrhosis) from the parameters (enzymes, proteins, and biomarker values) of blood samples as input variables with high classification accuracy using GPSC algorithm? * due to the high imbalance between class samples in the original dataset is it possible to apply different balancing methods (oversampling) to achieve a balance between class samples and used these datasets to obtain symbolic expressions using the GPSC algorithm with high classification accuracy? This paper consists of the following sections Materials and Methods, Results, Discussion, and Conclusion. In the Materials and Methods section, the research methodology is described as well as the dataset, oversampling techniques, GPSC, random hyperparameter search, one versus rest classifier, evaluation metrics and methodology and computational resources used in this research. In the Results section, the results obtained in the case of binary and multiclass classification are presented. Then the best symbolic expressions are shown in both cases with classification accuracy shown when these symbolic expressions are applied to the original dataset. In the Discussion section, the previously shown results are discussed and finally, in the Conclusions section, the conclusions are given based on the conducted investigation as an answer to hypotheses derived in this section.