Paper Info
Title
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 pratical significance.
Year
DOI
Venue
2021
10.1166/jmihi.2021.3306
JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS
Keywords
DocType
Volume
Medical Big Data, Tumor Disease, Data Mining, Apriori Algorithm, Service Model
Journal
11
Issue
ISSN
Citations 
2
2156-7018
0
PageRank 
References 
Authors
0.34
0
8
Name
Order
Citations
PageRank
Jing Zheng1308.57
Zhongjun Gao200.34
Lixin Pu300.34
Mingjie He410.68
Jipeng Fan500.34
Shuang Wang6326.49
Yunpeng Cai78216.87
Limin He800.34