Machine learning and graph technology have revolutionized medical research, particularly in the field of cancer. MicroRNAs, small non-coding RNAs that play a crucial role in cell differentiation and regulation, have emerged as promising biomarkers for diagnosing diseases. By analyzing microRNA expression patterns, researchers can identify specific types of cancer, including stomach cancer, which is one of the deadliest forms of cancer globally. A machine learning model trained on large datasets of microRNA-expression profiles has shown high accuracy in predicting gastric cancer, with an area under the curve of 0.8. This technology has the potential to improve diagnosis and treatment outcomes by detecting diseases at early stages, reducing mortality rates, and alleviating congested endoscopy services. The approach leverages graph technology to analyze vast amounts of medical literature, extract relationships between microRNAs, genes, and diseases, and build predictive models for disease diagnosis.