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Analysis of Factors Influencing the Development of mHealth Innovation Based on Data Mining Algorithms

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  • Rui Ma
  • Bin Liu
  • Baiyuan Ding

Abstract

Data mining algorithms combine expertise in machine algorithm learning, software modeling pattern recognition, statistical analysis principles, database construction, and artificial intelligence. With the rapid development of Internet technology and the common application of cell phones, mobile medical, a new medical method based on this technology, has been spawned, which greatly facilitates multiple aspects of medical services such as doctor diagnosis, patient treatment, disease care, and health management of critically ill patients and also alleviates the imbalance of medical resources. This paper firstly starts from the background of rapid development of information technology and mobile technology, combines the theoretical knowledge of data mining algorithm and mobile medical, as well as previous research reviews, and presents the main research content of this paper: analysis of factors influencing the development of mobile medical innovation based on data mining algorithm. Based on the K-means algorithm in the data mining algorithm and the Apriori algorithm in the association algorithm, this paper analyzes the current situation and problems of mobile medical development in China based on the algorithm model, analyzes the influencing factors of mobile medical innovation development in China based on the algorithm model, and summarizes and concludes the influencing factors of mobile medical innovation development and concludes that there are four categories of mHealth innovation development influencing factors: demand influence, policy orientation, technological innovation, and capital injection.

Suggested Citation

  • Rui Ma & Bin Liu & Baiyuan Ding, 2022. "Analysis of Factors Influencing the Development of mHealth Innovation Based on Data Mining Algorithms," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-13, August.
  • Handle: RePEc:hin:jnlmpe:4311937
    DOI: 10.1155/2022/4311937
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