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Classification of clustered health care data analysis using generative adversarial networks (GAN)

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Abstract

Innovation and adoption of new technologies in health care industries produce vast data every day. The diverse data in health care include clinical data, health history, and genetic data. Also, real-time monitoring in health care generates huge data, and efficiently examining these big data is a challenging task. Analysis of health care data becomes more important so that proper medications can be provided and issues can be reduced by taking proper precautions based on the history. Data analysis becomes efficient because of automation, however, due to data integrity, data diversity, and inconsistency, the performance gets lagged. Various machine learning models are introduced to handle big data management; however, researchers are still working to attain a better model with improved accuracy. So with the objective to attain maximum classification accuracy, fuzzy c means clustering and generative adversarial network are employed in this research work for health care data clustering and classification. Benchmark lung cancer dataset and Arrhythmia dataset are used in the experimentation. The proposed model exhibits the maximum accuracy of 97.8% for dataset 1 and 98.6% for dataset 2 compared to existing techniques like support vector machine, decision tree, and random forest algorithms.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Dr. N.G.P. Institute of Technology, Anna University, Coimbatore, TN, India

    N. Purandhar

  2. School of Computer Science and Engineering, REVA University, Bengaluru, Karnataka, India

    S. Ayyasamy

  3. Department of ECE, Dr. N. G. P. Institute of Technology, Coimbatore, India

    P. Siva Kumar

Authors
  1. N. Purandhar
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  2. S. Ayyasamy
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  3. P. Siva Kumar
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Correspondence to N. Purandhar.

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Humans and animals are not involved in this research work. We used our own data.

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Purandhar, N., Ayyasamy, S. & Siva Kumar, P. Classification of clustered health care data analysis using generative adversarial networks (GAN). Soft Comput 26, 5511–5521 (2022). https://doi.org/10.1007/s00500-022-07026-7

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