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The worst performing method was Decision Trees with an accuracy of 0.675, precision of 0.571, recall of 0.599, F1 score of 0.570 and ROC-AUC of 0.654 Conclusion: Using machine learning methods, we were able to predict the presence of fatty liver disease with high accuracy, precision, and recall

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Lazo G, Kantarjian H, Estey E, Thomas D, OBrien S, Cortes J (2003) Use of arsenic trioxide (As2O3) in the treatment of patients with acute promyelocytic leukemia: the M.D

Therefore, when assessing the health risks of MPs, it is essential to consider the combined effects of an individuals initial microbiota state, dietary structure, genetic susceptibility, and co-exposure to environmental factors (Fig

We demonstrated that at 0.4% hematocrit, there is an exacerbated increase in PS exposure and hemolysis induced by BAY 43-9006, when compared to 4% hematocrit ( Figure 1 )