D-TSVR Recurrence Prediction Driven by Medical Big Data in Cancer

Abstract

Secondary use of medical big data is becoming increasingly popular in healthcare services and clinical research in the medical industry. Cancer recurrence is a common phenomenon of cancer patients after treatment (recovery period). Studying the time and influencing factors of cancer recurrence can provide effective clinical intervention means, which is the gospel of cancer patients. In this article, a sample of 50 000 cases of seven cancer patients, including liver cancer, lung cancer, kidney cancer, breast cancer, uterine cancer, stomach cancer, and bowel cancer, was collected. A twin support regression vector machine (TSVR) algorithm based on dependent nearest neighbor (DNN) weighting is proposed, the eplion-TSVR model is improved by DNN-weighted algorithm with local information mining function, and the solution of the improved model is derived. It is proposed to use the cuckoo algorithm to determine the optimal parameters of DNN to determine the optimal dependency region domain. In this article, the improved TSVR algorithm is used to establish a cancer recurrence prediction model. The prediction accuracy of the model for various cancers can reach more than 91%, which is significantly higher than that of convolutional neural network and e-TSVR models.