Research on Early Warning Mechanism and Model of Liver Cancer Rehabilitation Based on CS-SVM.

Abstract

Since the 20th century, cancer has become one of the main diseases threatening human health. Liver cancer is a malignant tumor with extremely high clinical morbidity and fatality rate and easy recurrence after surgery. Research on the postoperative recurrence time and recurrence location of patients with liver cancer has a crucial influence on the postoperative intervention of patients. Evaluation of the clinical manifestations of patients after liver cancer surgery is conducted according to medical knowledge or national standards to determine the main factors affecting liver cancer rehabilitation. In order to better study the mechanism of liver cancer recurrence, this paper uses CS-SVM to predict the recurrence time of liver cancer patients, so as to timely intervene the patients. There are five evaluation indicators which are basic indicators, immune indicators, microenvironment indicators, psychological indicators, and nutritional indicators, respectively. This paper collects the clinical evaluation data of postoperative follow-up visits for patients with liver cancer in a hospital, improves the parameter selection process of the support vector machine by using the search ability of the cuckoo algorithm, and establishes an algorithm-optimized prediction model of support vector machine for the prognosis of liver cancer to predict the location and approximate time of recurrence. According to the clinical evaluation data of patients with liver cancer after surgery, logistics regression, BP neural network, and other related methods are used to predict the prognosis of liver cancer patients after surgery. The prediction effects of several methods are compared, and the superiority of the model is discussed. At the end of this article, we conducted an empirical analysis on the clinical evaluation data of patients with liver cancer after surgery. For the collected samples of 776 liver cancer recurrences after surgery, the established liver cancer prognosis outcome prediction model was used to predict the recurrence time and recurrence location, respectively. The mean square error of recurrence time prediction is 9.2101, which is much smaller than the prediction mean square error of BP neural network of 177.9451; the prediction accuracy of recurrence location is 95.7%, which is much higher than the 63.14% of logistic regression. The empirical analysis results show that the improved support vector machine model based on cuckoo established in this paper can effectively predict the time and location of cancer recurrence.