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CRDP: Chronic Renal Disease Prediction and Evaluation with Reduced Prominent Features

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Evolutionary Artificial Intelligence (ICEASSM 2017)

Part of the book series: Algorithms for Intelligent Systems ((AIS))

Abstract

The kidneys are the prominent organs which help in the removal of waste and toxic material from the body. Kidney malfunctioning occurs due to various reasons, but if certain symptoms are ignored and not treated on time, then it may lead to persistent malfunctioning leading to Chronic Renal Disease (CRD). This condition expedites kidney failure and, in turn, death if not attended appropriately. This work depicts the appropriate, relevant, and correlated attributes among all the attributes and reduction of features in the dataset using chi-squared test on the patients’ dataset for better detection and prediction of CRD. The CRDP algorithm is implemented, and the results are predominantly used in logistic regression and K-nearest neighbor classification techniques to enhance and improve their prediction accuracy on CRD.

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Author information

Authors and Affiliations

  1. Nitte Meenakshi Institute of Technology, Bengaluru, India

    G. U. Vasanthakumar

  2. CGI Inc., Bengaluru, India

    B. S. Impana

Authors
  1. G. U. Vasanthakumar
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  2. B. S. Impana
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Corresponding author

Correspondence to G. U. Vasanthakumar .

Editor information

Editors and Affiliations

  1. Faculty of Innovation and Technology, Taylor’s University, Subang Jaya, Selangor, Malaysia

    David Asirvatham

  2. University of Southeast Norway, Notodden, Norway

    Francisco M. Gonzalez-Longatt

  3. Gdansk University of Technology, Gdańsk, Poland

    Przemyslaw Falkowski-Gilski

  4. Professor of Computer Engineering, Papua New Guinea University of Technology, Lae, Papua New Guinea

    R. Kanthavel

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Vasanthakumar, G.U., Impana, B.S. (2024). CRDP: Chronic Renal Disease Prediction and Evaluation with Reduced Prominent Features. In: Asirvatham, D., Gonzalez-Longatt, F.M., Falkowski-Gilski, P., Kanthavel, R. (eds) Evolutionary Artificial Intelligence. ICEASSM 2017. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-8438-1_16

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