IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0295234.html
   My bibliography  Save this article

Chronic kidney disease prediction using boosting techniques based on clinical parameters

Author

Listed:
  • Shahid Mohammad Ganie
  • Pijush Kanti Dutta Pramanik
  • Saurav Mallik
  • Zhongming Zhao

Abstract

Chronic kidney disease (CKD) has become a major global health crisis, causing millions of yearly deaths. Predicting the possibility of a person being affected by the disease will allow timely diagnosis and precautionary measures leading to preventive strategies for health. Machine learning techniques have been popularly applied in various disease diagnoses and predictions. Ensemble learning approaches have become useful for predicting many complex diseases. In this paper, we utilise the boosting method, one of the popular ensemble learnings, to achieve a higher prediction accuracy for CKD. Five boosting algorithms are employed: XGBoost, CatBoost, LightGBM, AdaBoost, and gradient boosting. We experimented with the CKD data set from the UCI machine learning repository. Various preprocessing steps are employed to achieve better prediction performance, along with suitable hyperparameter tuning and feature selection. We assessed the degree of importance of each feature in the dataset leading to CKD. The performance of each model was evaluated with accuracy, precision, recall, F1-score, Area under the curve-receiving operator characteristic (AUC-ROC), and runtime. AdaBoost was found to have the overall best performance among the five algorithms, scoring the highest in almost all the performance measures. It attained 100% and 98.47% accuracy for training and testing sets. This model also exhibited better precision, recall, and AUC-ROC curve performance.

Suggested Citation

  • Shahid Mohammad Ganie & Pijush Kanti Dutta Pramanik & Saurav Mallik & Zhongming Zhao, 2023. "Chronic kidney disease prediction using boosting techniques based on clinical parameters," PLOS ONE, Public Library of Science, vol. 18(12), pages 1-21, December.
  • Handle: RePEc:plo:pone00:0295234
    DOI: 10.1371/journal.pone.0295234
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0295234
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0295234&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0295234?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. David K E Lim & James H Boyd & Elizabeth Thomas & Aron Chakera & Sawitchaya Tippaya & Ashley Irish & Justin Manuel & Kim Betts & Suzanne Robinson, 2022. "Prediction models used in the progression of chronic kidney disease: A scoping review," PLOS ONE, Public Library of Science, vol. 17(7), pages 1-24, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jihoon Moon & Muazzam Maqsood & Dayeong So & Sung Wook Baik & Seungmin Rho & Yunyoung Nam, 2024. "Advancing ensemble learning techniques for residential building electricity consumption forecasting: Insight from explainable artificial intelligence," PLOS ONE, Public Library of Science, vol. 19(11), pages 1-36, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Eiichiro Kanda & Bogdan I Epureanu & Taiji Adachi & Tamaki Sasaki & Naoki Kashihara, 2024. "Mathematical expansion and clinical application of chronic kidney disease stage as vector field," PLOS ONE, Public Library of Science, vol. 19(3), pages 1-16, March.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0295234. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.