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. 2022 Jan 16;12(1):212. doi: 10.3390/diagnostics12010212

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© 2022 by the authors.

Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

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Positive and negative impact explanation of the top 15 features for predicting insulin resistance (IR) using SHAP values. (a) Explanation of each feature impact on the IR in the prediction model by the SHAP values in the XGBoost algorithm. (b) Explanation of each feature impact on the IR in the prediction model by the SHAP values in random forest algorithm. ALT, alanine aminotransferase; HbA1c, hemoglobin A1c; γ-GTP, γ-glutamyl transpeptidase; HDL, high-density lipoprotein; CRP, high-sensitive C-reactive protein; PRS, polygenetic risk scores.

Positive and negative impact explanation of the top 15 features for predicting insulin resistance (IR) using SHAP values. (a) Explanation of each feature impact on the IR in the prediction model by the SHAP values in the XGBoost algorithm. (b) Explanation of each feature impact on the IR in the prediction model by the SHAP values in random forest algorithm. ALT, alanine aminotransferase; HbA1c, hemoglobin A1c; γ-GTP, γ-glutamyl transpeptidase; HDL, high-density lipoprotein; CRP, high-sensitive C-reactive protein; PRS, polygenetic risk scores.