Assessment of Machine Learning Approaches to Predict in-Hospital Mortality in Patients Underwent Prosthetic Heart Valve Replacement Surgery.
Background & Objective: Machine learning and artificial intelligence are useful tools to analyze data with multiple variables. It has been shown that the prediction models obtained by Machine learning have better performance than the conventional statistical methods. This study was aimed to assess t...
| Publicado en: | Journal of Advances in Medical & Biomedical Research Vol. 31; no. 146; pp. 210 - 221 |
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| Autores principales: | , , , , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Zanjan University of Medical Sciences & Health Services
May/Jun2023
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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=169838466&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 169838466 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 26766264 N5MJ jtl: Journal of Advances in Medical & Biomedical Research issn: 26766264 maglogo: N pubinfo: dt: May/Jun2023 vid: 31 iid: 146 pid: 65276 pub: Zanjan University of Medical Sciences & Health Services artinfo: ui: 169838466 169838466 169838466 10.30699/jambs.31.146.210 169838466 ppf: 210 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Assessment of Machine Learning Approaches to Predict in-Hospital Mortality in Patients Underwent Prosthetic Heart Valve Replacement Surgery. aug: au: Shojaeifard, Maryam Ahangar, Hassan Gohari, Sepehr Oveisi, Mehrdad Maleki, Majid Reshadmanesh, Tara Arsang-Jang, Shahram Mahjani, Mahsa Pourkeshavarz, Mozhgan Hajianfar, Ghasem Mazloomzadeh, Saeedeh Shiri, Isaac Gohari, Sheida affil: Echocardiography Research Center, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran. sug: subj: Heart Valve Diseases Surgery Heart Valve Prosthesis Adverse Effects Machine Learning Evaluation Hospital Mortality Risk Factors Risk Assessment Postoperative Complications Risk Factors Human Male Female Retrospective Design Cross Sectional Studies Univariate Statistics Algorithms Descriptive Statistics ROC Curve Odds Ratio Confidence Intervals Male Female ab: Background & Objective: Machine learning and artificial intelligence are useful tools to analyze data with multiple variables. It has been shown that the prediction models obtained by Machine learning have better performance than the conventional statistical methods. This study was aimed to assess the risk factors and determine the best machine learning prediction model/s for in-hospital mortality among patients who underwent prosthetic valve replacement surgery. Materials & Methods: In this retrospective cross-sectional study, patient’s preoperative, intra-operative and post-operative data underwent univariate analysis. Feature importance determination was carried out using algorithms including principal component analysis (PCA), support vector machine (SVM), random forest (RF) model-based, and recursive feature elimination (RFE). Then, 13 machine learning classifiers were implemented for in-hospital prediction model. Results: The In-hospital mortality rate was 6.36%. Data from 2455 patients underwent final analysis. The machine learning results revealed that among preoperative features, Adaptive boost (AB) and RF classifiers (AUC: 0.82±0.033; 0.78±0.028, respectively); among intra-operative features, AB and K-nearest neighbors (KNN) classifiers (AUC: 0.68±0.014); among postoperative features, AB and RF classifiers (AUC: 0.9±0.1; 0.88±0.095, respectively); and among all features, AB and LR classifiers (AUC: 0.93±0.049; 0.93±0.055, respectively) had the best performance in prediction of in-hospital mortality. Conclusion: The AB classifier was determined as the best model in prediction of in-hospital mortality in all 4 datasets. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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