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MapReduce distributed parallel computing framework for diagnosis and treatment of knee joint Kashin-Beck disease

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Abstract

To improve the accuracy and computational efficiency of the MapReduce distributed parallel computing framework, thereby mining the diagnosis and treatment data of Kashin-Beck Disease (KBD) of the knee joint. Based on the shortcomings of the traditional K-means Clustering Algorithm (KCA), a simplified method for distance calculation was proposed. The Manhattan distance was used instead of Euclidean distance. Further improvement strategies were proposed to implement and compare KCA of MapReduce (MR-KCA) and Improved MR-KCA (IMR-KCA). With the same data, the sum of squared errors of MR-KCA and IMR-KCA decreased with the increase in the number of center points. Compared with MR-KCA, the quality of IMR-KCA was higher, and their difference was especially evident at 8 GB data capacity. The total execution time of both MR-KCA and IMR-KCA increased with the increase in the number of center points. Compared to MR-KCA, the total execution time of IMR-KCA was significantly reduced, especially when the data capacity was 8 GB. When the number of center points was 5000, IMR-KCA could reduce the total execution time by 50%. Through experiments, IMR-KCA was proved to better present the diagnosis and treatment data of patients with knee joint KBD. The scalability rates of MR-KCA and IMR-KCA decreased as the number of nodes increased, but the scalability rates of both algorithms could be maintained above 0.80, which had better scalability. Compared with MR-KCA, IMR-KCA had significantly higher scalability. The IMR-KCA proposed in this study had high accuracy and computing efficiency, which could be used in the visualization of KBD diagnosis and treatment.

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Acknowledgements

This work was supported by Fund of Gansu Health Care Research Plan (GSWSKY-2019-12).

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Author notes

  1. Chenpo Dang, Guirong Yi and Zhaomin Zhu have equally contributed to this works.

Authors and Affiliations

  1. Department of Orthopedics (Sport Medicine), The 940th Hospital of Joint Logistics Support Force of Chinese People’s Liberation Army, Lanzhou, 730050, People’s Republic of China

    Chenpo Dang, Peng Zhou, Hongbin Shao, Yanbin Yao, Maosheng Zhao & Shensong Li

  2. Department of Gastroenterology, The Second Affiliated Hospital of Lanzhou University, Lanzhou, 730050, People’s Republic of China

    Guirong Yi

  3. Department of Orthopedics, General Hospital of Tibet Military Region, Lasa, 850007, People’s Republic of China

    Zhaomin Zhu

  4. Clinical Medical Colleges, Gansu University of Chinese Medicine, Lanzhou, 730050, People’s Republic of China

    Maosheng Zhao

  5. Department of Orthopedics, School of Medicine, Jinling Hospital, Nanjing University, Nanjing, 210093, People’s Republic of China

    Lintao Li

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  1. Chenpo Dang
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  2. Guirong Yi
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  7. Maosheng Zhao
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Correspondence to Lintao Li or Shensong Li.

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Dang, C., Yi, G., Zhu, Z. et al. MapReduce distributed parallel computing framework for diagnosis and treatment of knee joint Kashin-Beck disease. J Supercomput 77, 9088–9101 (2021). https://doi.org/10.1007/s11227-020-03608-0

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  • DOI: https://doi.org/10.1007/s11227-020-03608-0

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