Research And Implementation Of Prediction Method For Pancreatic Cancer Metastasis Based On Machine Learning

Pancreatic cancer is a gastrointestinal tumor that is highly susceptible to metastasis,and 40% to 50% of patients have already developed tumor metastasis by the time they are diagnosed with pancreatic cancer.Surgical resection is the only potentially curative treatment,but a large number of patients still develop metastases after surgery,with a postoperative survival rate of less than 20%.The search for biomarkers with high specificity and sensitivity can enable targeted therapy for patients.Biomarker-based studies can provide effective molecular targets for early screening and precise treatment of metastatic patients.Meanwhile,the combination of machine learning algorithms to construct a classification model for pancreatic cancer metastasis to predict the metastatic risk of postoperative patients can achieve early intervention for patients with metastatic risk after surgery and improve the survival rate of patients.Studies have shown that tumor metastasis is closely associated with epithelial mesenchymal transition(EMT),and the EMT process promotes tumor metastasis.Therefore,this paper focuses on EMT-related genes,combines bioinformatics methods to screen pancreatic cancer metastasis biomarkers,uses machine learning methods to construct prediction models for pancreatic cancer metastasis,and conducts optimization studies on support vector machine classification algorithms.The research work and results of this paper are as follows:(1)Bioinformatics and machine learning based approach to explore pancreatic cancer metastasis.Nine EMT biomarkers in pancreatic cancer metastasis,namely S100A2(S100 Calcium Binding Protein A2)、 ZBED2(Zinc Finger BED-Type Containing 2)、LAMC2(Laminin Subunit Gamma 2)、TGM2(Transglutaminase 2)、HMGA2(High Mobility Group Protein HMGI-C)、LOXL2(Lysyl Oxidase Like 2)、TGFBI(Transforming Growth Factor Beta Induced)、JAG1(Protein Jagged-1)and HLF(HLF Transcription Factor),were screened in a hierarchical manner using bioinformatic methods.Functional enrichment analysis is used to explore the underlying biological mechanisms of pancreatic cancer metastasis.We found that pancreatic cancer metastasis is associated with pathways such as epithelial mesenchymal transformation,cell adhesion,extracellular matrix organization,endothelial cell migration,vascular remodeling,and chondrocyte differentiation.Prediction models for pancreatic cancer metastasis were constructed based on biomarker expression data using four traditional machine learning algorithms: random forest(RF),support vector machine(SVM),logistic regression(LR),and K-nearest neighbor(KNN).A comprehensive comparison of the accuracy,precision,recall,F1 score,and AUC of the models revealed that the classification models constructed by the support vector machine algorithm with Gaussian kernel function had better performance than other traditional machine learning models.(2)In this paper,we study Gaussian kernel functions in support vector machine kernel functions to optimize the performance of pancreatic cancer metastasis prediction models.Based on the distribution of Gaussian kernel function curves,it is found that the Gaussian kernel function has a weak generalization ability far from the test point,so two displacement parameters are added to the Gaussian kernel function to form the improved Gaussian kernel function.In addition,it is shown that the hybrid kernel works better than the single kernel model,and the improved Gaussian kernel function is combined with polynomial and Sigmoid kernel functions using the idea of hybrid kernel function to improve the adaptability of the model and the classification prediction effect.(3)Design and implementation of a pancreatic cancer metastasis early prediction system.Improved machine learning algorithms are used as predictive models.The system combines data analysis processing and prediction for user interaction.The study was conducted to predict pancreatic cancer patients who are still at risk of metastasis after surgery and then prophylactic drug administration.The work in this paper is based on the above three modules to mine the data of pancreatic cancer patients to find the EMT biomarkers associated with pancreatic cancer metastasis and predict pancreatic cancer metastasis,in order to achieve early detection and treatment of pancreatic cancer metastasis patients and improve their prognosis.