IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v17y2020i3p897-d314955.html
   My bibliography  Save this article

Comparing Logistic Regression Models with Alternative Machine Learning Methods to Predict the Risk of Drug Intoxication Mortality

Author

Listed:
  • YoungJin Choi

    (Department of Healthcare Administration, Eulji University, Seongnam 13135, Korea)

  • YooKyung Boo

    (Department of Health Administration, Dankook University, Cheonan 31116, Korea)

Abstract

(1) Medical research has shown an increasing interest in machine learning, permitting massive multivariate data analysis. Thus, we developed drug intoxication mortality prediction models, and compared machine learning models and traditional logistic regression. (2) Categorized as drug intoxication, 8,937 samples were extracted from the Korea Centers for Disease Control and Prevention (2008-2017). We trained, validated, and tested each model through data and compared their performance using three measures: Brier score, calibration slope, and calibration-in-the-large. (3) A chi-square test demonstrated that mortality risk statistically significantly differed according to severity, intent, toxic substance, age, and sex. The multilayer perceptron model (MLP) had the highest area under the curve (AUC), and lowest Brier score in training and validation phases, while the logistic regression model (LR) showed the highest AUC (0.827) and lowest Brier score (0.0307) in the testing phase. MLP also had the second-highest AUC (0.816) and second-lowest Brier score (0.003258) in the testing phase, demonstrating better performance than the decision-making tree model. (4) Given the complexity of choosing tuning parameters, LR proved competitive when using medical datasets, which require strict accuracy.

Suggested Citation

  • YoungJin Choi & YooKyung Boo, 2020. "Comparing Logistic Regression Models with Alternative Machine Learning Methods to Predict the Risk of Drug Intoxication Mortality," IJERPH, MDPI, vol. 17(3), pages 1-10, January.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:3:p:897-:d:314955
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/17/3/897/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/17/3/897/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Joon Lee & David M Maslove & Joel A Dubin, 2015. "Personalized Mortality Prediction Driven by Electronic Medical Data and a Patient Similarity Metric," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-13, May.
    2. Rockett, I.R.H. & Smith, G.S. & Caine, E.D. & Kapusta, N.D. & Hanzlick, R.L. & Larkin, G.L. & Naylor, C.P.E. & Nolte, K.B. & Miller, T.R. & Putnam, S.L. & De Leo, D. & Kleinig, J. & Stack, S. & Todd, , 2014. "Confronting death from drug self-intoxication (DDSI): Prevention through a better definition," American Journal of Public Health, American Public Health Association, vol. 104(12), pages 49-55.
    3. Helmreich, James E., 2016. "Regression Modeling Strategies with Applications to Linear Models, Logistic and Ordinal Regression and Survival Analysis (2nd Edition)," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 70(b02).
    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. Faheem Aslam & Tahir Mumtaz Awan & Tayyba Fatima, 2022. "Classification of m-payment users’ behavior using machine learning models," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 27(3), pages 264-275, September.

    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. Lucas Wirbka & Walter E Haefeli & Andreas D Meid, 2020. "A framework to build similarity-based cohorts for personalized treatment advice – a standardized, but flexible workflow with the R package SimBaCo," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-12, May.
    2. Gheyath Chalabi & Hussein Dia, 2024. "Telecommuting and Travel Behaviour: A Survey of White-Collar Employees in Adelaide, Australia," Sustainability, MDPI, vol. 16(7), pages 1-28, March.
    3. Pascal Caillet & Maud Poirier & Marie Grall-Bronnec & Edouard Marchal & Alain Pineau & Catherine Pintas & Véronique Carton & Pascale Jolliet & Norbert Winer & Caroline Victorri-Vigneau, 2019. "High prevalence of kaolin consumption in migrant women living in a major urban area of France: A cross-sectional investigation," PLOS ONE, Public Library of Science, vol. 14(7), pages 1-14, July.
    4. Christos T Nakas & Narayan Schütz & Marcus Werners & Alexander B Leichtle, 2016. "Accuracy and Calibration of Computational Approaches for Inpatient Mortality Predictive Modeling," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-11, July.
    5. Christopher J. Ruhm, 2016. "Taking the Measure of a Fatal Drug Epidemic," NBER Working Papers 22504, National Bureau of Economic Research, Inc.

    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:gam:jijerp:v:17:y:2020:i:3:p:897-:d:314955. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.