XGBoost, A Novel Explainable AI Technique, in the Prediction of Myocardial Infarction: A UK Biobank Cohort Study.
We wanted to assess if "Explainable AI" in the form of extreme gradient boosting (XGBoost) could outperform traditional logistic regression in predicting myocardial infarction (MI) in a large cohort. Two machine learning methods, XGBoost and logistic regression, were compared in predicting risk of M...
| Publicado en: | Clinical Medicine Insights: Cardiology Vol. 16; pp. 1 - 7 |
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| Autores principales: | , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
11/8/2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=160110608&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 160110608 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 11795468 B3KL jtl: Clinical Medicine Insights: Cardiology issn: 11795468 maglogo: Y pubinfo: dt: 11/8/2022 vid: 16 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 160110608 160110608 160110608 10.1177/11795468221133611 160110608 ppf: 1 ppct: 6 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: XGBoost, A Novel Explainable AI Technique, in the Prediction of Myocardial Infarction: A UK Biobank Cohort Study. aug: au: Moore, Alexander Bell, Max affil: Head of Data Science at Managed Self Limited, London, England, UK sug: subj: Myocardial Infarction Risk Factors Risk Assessment Methods Artificial Intelligence Human Tissue Banks United Kingdom Prospective Studies Logistic Regression Adult Middle Age Aged ROC Curve Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years ab: We wanted to assess if "Explainable AI" in the form of extreme gradient boosting (XGBoost) could outperform traditional logistic regression in predicting myocardial infarction (MI) in a large cohort. Two machine learning methods, XGBoost and logistic regression, were compared in predicting risk of MI. The UK Biobank is a population-based prospective cohort including 502 506 volunteers with active consent, aged 40 to 69 years at recruitment from 2006 to 2010. These subjects were followed until end of 2019 and the primary outcome was myocardial infarction. Both models were trained using 90% of the cohort. The remaining 10% was used as a test set. Both models were equally precise, but the regression model classified more of the healthy class correctly. XGBoost was more accurate in identifying individuals who later suffered a myocardial infarction. Receiver operator characteristic (ROC) scores are class size invariant. In this metric XGBoost outperformed the logistic regression model, with ROC scores of 0.86 (accuracy 0.75 (CI ±0.00379) and 0.77 (accuracy 0.77 (CI ± 0.00369) respectively. Secondly, we demonstrate how SHAPley values can be used to visualize and interpret the predictions made by XGBoost models, both for the cohort test set and for individuals. The XGBoost machine learning model shows very promising results in evaluating risk of MI in a large and diverse population. This model can be used, and visualized, both for individual assessments and in larger cohorts. The predictions made by the XGBoost models, points toward a future where "Explainable AI" may help to bridge the gap between medicine and data science. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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