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Challenges in the application of Machine Learning algorithms in Biomedical Research

Lookup NU author(s): Melvin JoyORCiD

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Abstract

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.


Publication metadata

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


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