PreCKD_ML: Machine Learning Based Development of Prediction Model for Chronic Kidney Disease and Identify Significant Risk Factors | SpringerLink
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PreCKD_ML: Machine Learning Based Development of Prediction Model for Chronic Kidney Disease and Identify Significant Risk Factors

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Machine Intelligence and Emerging Technologies (MIET 2022)

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Abstract

Chronic Kidney Disease (CKD) is major concern of death in recent years that can be cured by early treatment and proper supervision. But early detection of CKD and exact risk factors should be known to ensure proper treatment. The study mainly aims to address the issue by building a predictive model and discovers the most significant risk factors employing machine learning (ML) approach for CKD patients. Four individual machine learning classifiers were applied to conduct this study. It is found that GB performed very poor compare to other applied classifiers where RF and LightGBM outperformed with 99.167% accuracy. In terms of risk factors, it is found that sg, hemo, sc, pcv, al, rbcc, htn, dm, bgr, and sod are the most significant factors, which are mainly correlated with CKD. The study and its findings indicate that it will enable patients, doctors and clinicians to identify CKD patients early and ensure proper treatment for them.

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Acknowledgement

This work was supported by funding from the Natural Sciences and Engineering Research Council of Canada (NSERC)

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Authors and Affiliations

  1. Department of Software Engineering (SWE), Daffodil International University (DIU), Daffodil Smart City, Ashulia, Savar, Dhaka, 1341, Bangladesh

    Md. Rajib Mia, Md. Ashikur Rahman & Md. Mamun Ali

  2. Group of Biophotomatix, Department of ICT, Mawlana Bhashani Science and Technology University (MBSTU), Santosh, Tangail, 1902, Bangladesh

    Kawsar Ahmed

  3. Department of Electrical and Computer Engineering (ECE), University of Saskatchewan (USASK), 57 Campus Drive, Saskatoon, SK, S7N 5A9, Canada

    Kawsar Ahmed & Francis M. Bui

  4. Department of Computer Science, American International University-Bangladesh (AIUB), Kuratoli, Dhaka, 1229, Bangladesh

    S M Hasan Mahmud

Authors
  1. Md. Rajib Mia
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  2. Md. Ashikur Rahman
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  3. Md. Mamun Ali
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  4. Kawsar Ahmed
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  5. Francis M. Bui
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  6. S M Hasan Mahmud
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Corresponding author

Correspondence to Kawsar Ahmed .

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Editors and Affiliations

  1. Noakhali Science and Technology University, Noakhali, Bangladesh

    Md. Shahriare Satu

  2. The University of Queensland, St. Lucia, QLD, Australia

    Mohammad Ali Moni

  3. Jahangirnagar University, Dhaka, Bangladesh

    M. Shamim Kaiser

  4. Daffodil International University, Dhaka, Bangladesh

    Mohammad Shamsul Arefin

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Mia, M.R., Rahman, M.A., Ali, M.M., Ahmed, K., Bui, F.M., Mahmud, S.M.H. (2023). PreCKD_ML: Machine Learning Based Development of Prediction Model for Chronic Kidney Disease and Identify Significant Risk Factors. In: Satu, M.S., Moni, M.A., Kaiser, M.S., Arefin, M.S. (eds) Machine Intelligence and Emerging Technologies. MIET 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 490. Springer, Cham. https://doi.org/10.1007/978-3-031-34619-4_10

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  • DOI: https://doi.org/10.1007/978-3-031-34619-4_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-34618-7

  • Online ISBN: 978-3-031-34619-4

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