IDEAS home Printed from https://ideas.repec.org/a/kap/hcarem/v25y2022i3d10.1007_s10729-022-09597-1.html
   My bibliography  Save this article

Non-linear missing data imputation for healthcare data via index-aware autoencoders

Author

Listed:
  • Sadaf Kabir

    (West Virginia University)

  • Leily Farrokhvar

    (California State University Northridge)

Abstract

The availability of data in the healthcare domain provides great opportunities for the discovery of new or hidden patterns in medical data, which can eventually lead to improved clinical decision making. Predictive models play a crucial role in extracting this unknown information from data. However, medical data often contain missing values that can degrade the performance of predictive models. Autoencoder models have been widely used as non-linear functions for the imputation of missing data in fields such as computer vision, transportation, and finance. In this study, we assess the shortcomings of autoencoder models for data imputation and propose modified models to improve imputation performance. To evaluate, we compare the performance of the proposed model with five well-known imputation techniques on six medical datasets and five classification methods. Through extensive experiments, we demonstrate that the proposed non-linear imputation model outperforms the other models for all degrees of missing ratios and leads to the highest disease classification accuracy for all datasets.

Suggested Citation

  • Sadaf Kabir & Leily Farrokhvar, 2022. "Non-linear missing data imputation for healthcare data via index-aware autoencoders," Health Care Management Science, Springer, vol. 25(3), pages 484-497, September.
    • Handle: RePEc:kap:hcarem:v:25:y:2022:i:3:d:10.1007_s10729-022-09597-1
    DOI: 10.1007/s10729-022-09597-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10729-022-09597-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10729-022-09597-1?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ghosh, Anil K., 2006. "On optimum choice of k in nearest neighbor classification," Computational Statistics & Data Analysis, Elsevier, vol. 50(11), pages 3113-3123, July.
    2. Yuzhe Liu & Vanathi Gopalakrishnan, 2017. "An Overview and Evaluation of Recent Machine Learning Imputation Methods Using Cardiac Imaging Data," Data, MDPI, vol. 2(1), pages 1-15, January.
    3. Tutz, Gerhard & Ramzan, Shahla, 2015. "Improved methods for the imputation of missing data by nearest neighbor methods," Computational Statistics & Data Analysis, Elsevier, vol. 90(C), pages 84-99.
    4. Mohammadreza Torkjazi & Leily Kamali Farrokhvar & Behrooz Kamali, 2022. "Main Contributing Factors and the Heuristic Approach for Assessing Risk at Mass Gatherings," SN Operations Research Forum, Springer, vol. 3(1), pages 1-26, March.
    Full references (including those not matched with items on IDEAS)

    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. Daudin, Jean-Jacques & Mary-Huard, Tristan, 2008. "Estimation of the conditional risk in classification: The swapping method," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 3220-3232, February.
    2. Yinglei Lai & Baolin Wu & Hongyu Zhao, 2011. "A permutation test approach to the choice of size k for the nearest neighbors classifier," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(10), pages 2289-2302.
    3. Giuseppe Nuti, 2019. "An Efficient Algorithm for Bayesian Nearest Neighbours," Methodology and Computing in Applied Probability, Springer, vol. 21(4), pages 1251-1258, December.
    4. Angela Gorgoglione & Alberto Castro & Christian Chreties & Lorena Etcheverry, 2020. "Overcoming Data Scarcity in Earth Science," Data, MDPI, vol. 5(1), pages 1-5, January.
    5. Laha, A. K. & Rathi, Poonam, 2017. "New Approaches to Prediction using Functional Data Analysis," IIMA Working Papers WP 2017-08-02, Indian Institute of Management Ahmedabad, Research and Publication Department.
    6. Marlene A. Perez-Villalpando & Kelly J. Gurubel Tun & Carlos A. Arellano-Muro & Fernando Fausto, 2021. "Inverse Optimal Control Using Metaheuristics of Hydropower Plant Model via Forecasting Based on the Feature Engineering," Energies, MDPI, vol. 14(21), pages 1-18, November.
    7. Faisal Shahla & Tutz Gerhard, 2017. "Missing value imputation for gene expression data by tailored nearest neighbors," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 16(2), pages 95-106, April.
    8. Li, Jing & Gao, Fei & Lin, Song & Guo, Mingchao & Li, Yongmei & Liu, Hailing & Qin, Sujuan & Wen, QiaoYan, 2023. "Quantum k-fold cross-validation for nearest neighbor classification algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 611(C).

    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:kap:hcarem:v:25:y:2022:i:3:d:10.1007_s10729-022-09597-1. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.