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Lookup NU author(s): Melvin JoyORCiD
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In recent years, the application of machine learning (ML) algorithms has increased rapidly in various domains. Extensively in assisting diagnosis and predicting the prognosis in health care research. However, the challenges in using these methods are less understood by the researchers. The aim of this article is to present the following challenges in using ML algorithms in biomedical research. The use of ‘variable of importance’ in the prediction as ML models do not provide coefficients or weights, relation to regression coefficients and predicting the diagnosis or prognosis of low prevalence (imbalance) diseases, and the adjustment to handle this imbalance using Synthetic Minority Oversampling Technique called SMOTE, etc. Also, highlighted that the model selection with maximum accuracy or area under curve (AUC) statistics is alone not sufficient. The need for predictive values at various prevalence of outcome has to be highlighted. Simulation studies are recommended to evaluate the usefulness of SMOTE. The results of studies with the diseases prevalence 40% to 60% have to be used cautiously. Literature examples have been used to highlight the challenges.
Author(s): S M, Mani T, Joy M, Babu M, Jeyaseelan L
Publication type: Article
Publication status: Published
Journal: Journal of Applied Statistics and Machine Learning
Year: 2022
Volume: 1
Issue: 2
Pages: 117-126
Online publication date: 26/12/2022
Acceptance date: 25/10/2022
ISSN (electronic): 2583-2891
Publisher: ESI Publications
URL: https://www.esijournals.com/jasml/issue/74
