Solving the prognostic problem of hepatocellular carcinoma using machine learning algorithms in a national database

The emergence of digital medicine has paved the way for expanding research aimed at providing physicians with information support in medical decision-making and preventing medical errors.This trend has made the application of artificial intelligence methods highly relevant in preventing the spread, early diagnosis, and prognosis of hepatocellular carcinoma (HCC), which ranks third globally in cancer-related deaths.In this study, machine learning algorithms such as Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF) are applied to predict HCC using the HCC Dataset obtained from the Kaggle platform.A prediction algorithm based on the national HCC dataset is proposed by utilizing the RF method, which yielded the most significant results.The MICE algorithm is applied to impute the missing values in the national HCC database, and data from this database are gradually transferred to the Kaggle HCC Dataset (where prediction outcomes are known) to obtain prognostic results based on clinical patient data.