A Study of Bias in Machine Learning Techniques in Predicting Non-Alcoholic Fatty Liver Disease (NAFLD)

Non-alcoholic fatty liver disease (NAFLD) affects 24% of adults in the US, about 25% of the global population, and about 9-32% of adults in India. There is no definitive procedure for diagnosing NAFLD. Instead, there is only hope that physicians discover an abnormal blood test during a yearly checkup before the disease progresses to a stage where liver damage is permanent. This study aims to demonstrate the relevance of race and ethnicity when investigating NAFLD risk factors to include in prediction models. Mann-Whitney U tests show how BMI, TG, and ALT of races and ethnicities of those who were later diagnosed with NAFLD differ greatly implying that not including race and ethnicity may lead to inaccurate and non-generalizable prediction models. Advisor: Soundar Kumara, Paul Griffin

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Work Title A Study of Bias in Machine Learning Techniques in Predicting Non-Alcoholic Fatty Liver Disease (NAFLD)
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Penn State
Creators
  1. Mary Ogidigben
Keyword
  1. NAFLD
  2. Non-Alcoholic Fatty Liver Disease
  3. Machine Learning
  4. Bias
License In Copyright (Rights Reserved)
Work Type Research Paper
Acknowledgments
  1. I would like to acknowledge my co-advisors, Dr. Soundar Kumara and Dr. Paul Griffin, for their continued guidance and support.
  2. The project described was supported by the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant UL1 TR002014. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
  3. This work was supported by the National Institute of Standards and Technology (ror.org/05xpvk416). Mary Ogidigben was supported through PREP agreement no. 60NANB19D107 between NIST and the Pennsylvania State University (ror.org/04p491231).
Publication Date November 2023
Deposited November 03, 2023

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    Acknowledgments
    • I would like to acknowledge my co-advisors, Dr. Soundar Kumara and Dr. Paul Griffin, for their continued guidance and support., The project described was supported by the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant UL1 TR002014. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH., This work was supported by the National Institute of Standards and Technology (ror.org/05xpvk416). Mary Ogidigben was supported through PREP agreement no. 60NANB19D107 between NIST and the Pennsylvania State University (ror.org/04p491231).
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