| abstract |
The invention relates to a liver cancer risk prediction method, system, equipment and medium. The method includes acquiring Raman detection data of plasma samples; inputting the Raman detection data into a pre-trained artificial intelligence liver cancer risk prediction model based on BP neural network algorithm , to obtain liver cancer risk prediction results. The advantage is that the combination of Raman detection and liver cancer risk prediction model can greatly shorten the detection time, and the liver cancer risk prediction result can be obtained in about 15 minutes, and the subsequent accurate detection can be determined according to the prediction result; The test can detect a variety of substances at the same time, and the prediction results of liver cancer occurrence and metastasis risk are highly accurate; the detection cost is low, and medical waste is rarely generated, avoiding environmental pollution; the prediction method has high specificity, high sensitivity, and high accuracy; The rapid large-scale detection of small molecule metabolites has the advantages of high throughput and high accuracy, and the detection cost is relatively low. |