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  1. Predicting Asthma Attacks with Machine Learning: Insights from a Large US Study
  2. Shapely additive values can effectively visualize pertinent covariates in machine learning when predicting hypertension
  3. Exploring Depression and Nutritional Covariates Amongst US Adults using Shapely Additive Explanations
  4. Evaluating Apgar Scores as Predictors of Newborn Health Issues
  5. Increased vigorous exercise and decreased sedentary activities are associated with decreased depressive symptoms in United States adults: Analysis of The National Health and Nutrition Examination Survey (NHANES) 2017–2020
  6. Using Advanced Forecasting Models to Understand Obesity Trends in U.S. Adults
  7. Dendrogram of transparent feature importance machine learning statistics to classify associations for heart failure: A reanalysis of a retrospective cohort study of the Medical Information Mart for Intensive Care III (MIMIC-III) database
  8. Diabetes is associated with increased risk of death in COVID‐19 hospitalizations in Mexico 2020: A retrospective cohort study
  9. Use of machine learning to identify risk factors for insomnia
  10. Computation of the distribution of model accuracy statistics in machine learning: Comparison between analytically derived distributions and simulation‐based methods
  11. Hospitalized COVID‐19 patients with diabetes have an increased risk for pneumonia, intensive care unit requirement, intubation, and death: A cross‐sectional cohort study in Mexico in 2020
  12. Using simulations and explanations to make medical machine learning more reliable for heart disease