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Our datasets were obtained from the US National Health and Nutrition Examination Survey (NHANES, 2013\u20132020.3). The authors developed 5 ML models for hypertension identification by heavy metal exposure, and tested them by 10 discrimination characteristics. Further, the authors chose the optimally performing model after parameter adjustment by Genetic Algorithm (GA) for identification. Finally, in order to visualize the model's ability to make decisions, the authors used SHapley Additive exPlanation (SHAP) and Local Interpretable Model\u2010Agnostic Explanations (LIME) algorithm to illustrate the features. The study included 19\u00a0368 participants in total. A best\u2010performing eXtreme Gradient Boosting (XGB) with GA for hypertension identification by 16 heavy metals was selected (AUC: 0.774; 95% CI: 0.772\u20130.776; accuracy: 87.7%). According to SHAP values, Barium (0.02), Cadmium (0.017), Lead (0.017), Antimony (0.008), Tin (0.007), Manganese (0.006), Thallium (0.004), Tungsten (0.004) in urine, and Lead (0.048), Mercury (0.035), Selenium (0.05), Manganese (0.007) in blood positively influenced the model, while Cadmium (\u22120.001) in urine negatively influenced the model. Study participants' hypertension associated with heavy metal exposure was identified by an efficient, robust, and interpretable GA\u2010XGB model with SHAP and LIME. Barium, Cadmium, Lead, Antimony, Tin, Manganese, Thallium, Tungsten in urine, and Lead, Mercury, Selenium, Manganese in blood are positively correlated with hypertension, while Cadmium in blood is negatively correlated with hypertension.<\/jats:p>","DOI":"10.1111\/jch.14768","type":"journal-article","created":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T10:04:19Z","timestamp":1705053859000},"page":"187-196","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["The interpretable machine learning model associated with metal mixtures to identify hypertension via EMR mining method"],"prefix":"10.1111","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4317-6041","authenticated-orcid":false,"given":"Site","family":"Xu","sequence":"first","affiliation":[{"name":"Ruijin Hospital Shanghai Jiaotong University School of Medicine  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mu","family":"Sun","sequence":"additional","affiliation":[{"name":"Ruijin Hospital Shanghai Jiaotong University School of Medicine  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,1,12]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1111\/jch.14723"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41581\u2010019\u20100244\u20102"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2023.161691"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140\u20106736(21)01330\u20101"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.envres.2017.01.040"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.3390\/nu13082729"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12011\u2010018\u20101417\u20108"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/978\u20103\u2010319\u201095390\u20108_22"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.envres.2010.10.006"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11356\u2010021\u201017777\u20109"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.3390\/toxics10030116"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.chemosphere.2019.06.099"},{"key":"e_1_2_11_14_1","doi-asserted-by":"publisher","DOI":"10.3390\/metabo12121259"},{"key":"e_1_2_11_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00244\u2010019\u201000655\u20104"},{"key":"e_1_2_11_16_1","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2022.1039514"},{"key":"e_1_2_11_17_1","doi-asserted-by":"publisher","DOI":"10.3233\/THC\u2010220608"},{"key":"e_1_2_11_18_1","doi-asserted-by":"publisher","DOI":"10.1097\/MD.0000000000031763"},{"key":"e_1_2_11_19_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41746\u2010020\u20100229\u20103"},{"key":"e_1_2_11_20_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41746\u2010019\u20100193\u2010y"},{"key":"e_1_2_11_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajp.2022.103316"},{"key":"e_1_2_11_22_1","doi-asserted-by":"crossref","unstructured":"PengK MenziesT.Documenting evidence of a reuse of \u2018\u201cwhy should I trust you?\u201d: explaining the predictions of any classifier. 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