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Intelligent Data Engineering and Analytics pp 417–426Cite as

Statistical Granular Framework Towards Dealing Inconsistent Scenarios for Parkinson’s Disease Classification Big Data

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1177))

Abstract

While the medicinal and healthcare services sector is being changed by the competence to record gigantic measures of data about individual patients, the tremendous volume of information being gathered is outlandish for people to dissect/analyze. Over the past years, numerous techniques have been proposed so as to manage inconsistent data frameworks. Statistical applied ML facilitates an approach to consequently discover examples and reasoning about information. How one can transform raw data into valuable information that can empower healthcare professionals to make inventive automated clinical choices. The prior forecast and the location of disease cells can be profitable in curing the ailment in medical/healthcare appliances. This paper presents a novel statistical granular framework that deals with inconsistent instances, knowledge discovery, and further performs classification-based disease prediction. The experimental simulation is carried out on Parkinson’s disease classification dataset. The experimental results and comparative analysis with some significant existing approaches prove the novelty and optimality of our proposed prototype.

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Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, VIT University, Vellore, 632014, Tamilnadu, India

    D. Saidulu

  2. Department of Information Technology, Guru Nanak Institutions Technical Campus, Hyderabad, Telangana, India

    D. Saidulu

  3. Department of Computational Intelligence, School of Computer Science and Engineering, VIT University, Vellore, 632014, India

    R. Sasikala

Authors
  1. D. Saidulu
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  2. R. Sasikala
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Corresponding author

Correspondence to R. Sasikala .

Editor information

Editors and Affiliations

  1. School of Computer Engineering, Kalinga Institute Industrial Technology, Bhubaneswar, Odisha, India

    Suresh Chandra Satapathy

  2. Department of Informatics, University of Leicester, Leicester, UK

    Yu-Dong Zhang

  3. Department of Electronics and Communication Engineering, Shri Ramswaroop Memorial Group of Professional Colleges (SRMGPC), Lucknow, Uttar Pradesh, India

    Vikrant Bhateja

  4. School of Management, National Institute of Technology Karnataka, Surathkal, Karnataka, India

    Ritanjali Majhi

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© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Saidulu, D., Sasikala, R. (2021). Statistical Granular Framework Towards Dealing Inconsistent Scenarios for Parkinson’s Disease Classification Big Data. In: Satapathy, S., Zhang, YD., Bhateja, V., Majhi, R. (eds) Intelligent Data Engineering and Analytics. Advances in Intelligent Systems and Computing, vol 1177. Springer, Singapore. https://doi.org/10.1007/978-981-15-5679-1_39

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