Data Mining and Risk Prediction Based on Apriori Improved Algorithm for Lung Cancer

• Published in: Journal of signal processing systems Vol. 93; no. 7; pp. 795 - 809
• Main Authors: Guo, Hong, Liu, Hong, Chen, JiaYou, Zeng, Yan
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2021; Springer Nature B.V
• Subjects: Algorithms; Big Data; Circuits and Systems; Computer Imaging; Computer networks; Data mining; Distributed processing; Electrical Engineering; Electronic health records; Engineering; Image Processing and Computer Vision; Lung cancer; Medical records; Pattern Recognition; Pattern Recognition and Graphics; Signal,Image and Speech Processing; Technology assessment; Vision; Risk prediction; Medical big data; Analysis of pathogenic factors; Hadoop platform; Lung cancer data mining; Improved Apriori algorithm
• ISSN: 1939-8018; 1939-8115
• DOI: 10.1007/s11265-021-01663-1

Starting from medical big data, this article uses data mining technology to analyze and study the pathogenic factors of lung cancer based on the lung cancer electronic medical record data from the oncology department of the authoritative third grade A hospital for many years. With respect to the processing of huge data from electronic medical records for lung cancer, traditional serial Apriori algorithm has the disadvantages of scanning database frequently, running slowly and consuming large amount of memory resources. Therefore, an improved Apriori algorithm based on MapReduce distributed computing model of Hadoop platform is proposed. The experimental cluster and lung cancer data mining experiments show that the improved Apriori algorithm has higher execution efficiency and good system scalability in dealing with lung cancer big data, and can well mine the relationship between lung cancer and pathogenic factors, which has important guiding significance for assisting the clinical diagnosis and risk prediction of lung cancer.