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

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Abstract

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.

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Authors and Affiliations

  1. College of Computer Science and Technology Wuhan University of Science and Technology, Wuhan, China

    Hong Guo, Hong Liu & JiaYou Chen

  2. Intensive Care Unit, Wuhan third hospital, Wuhan, China

    Yan Zeng

Authors
  1. Hong Guo
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  2. Hong Liu
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  3. JiaYou Chen
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  4. Yan Zeng
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Correspondence to Hong Guo.

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Guo, H., Liu, H., Chen, J. et al. Data Mining and Risk Prediction Based on Apriori Improved Algorithm for Lung Cancer. J Sign Process Syst 93, 795–809 (2021). https://doi.org/10.1007/s11265-021-01663-1

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  • DOI: https://doi.org/10.1007/s11265-021-01663-1

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