Analysis of Tumor Disease Patterns Based on Medical Big Data

Abstract

Using the medical big data mining related technology, the model of tumor disease was analyzed and studied. Using data science methods as a guiding method and idea, analyzing and constructing a medical service model based on big data for oncology diseases, exploring its development strategy; using business process analysis method to analyze the business process and mapping of cancer disease medical services; using serviceoriented architecture analysis and Design methodology to build a highly flexible, configurable, and easily scalable precision medical big data platform. By analyzing the characteristics of medical big data and the shortcomings of the traditional Apriori algorithm, the Hadoop platform is used to improve and optimize the Apriori algorithm. The results show that the improved Apriori algorithm has great improvement in efficiency and performance, and can be adapted to mining medical big data. Through data mining experiments, it is concluded that there is a correlation between tumors and smoking, chronic infection, occupational pathogenic factors, etc. It has certain guiding significance for the prevention and treatment of tumors, thus also demonstrating the improved Apriori algorithm for lung tumors. Clinical research has practical significance.