Support vector machine deep mining of electronic medical records to predict the prognosis of severe acute myocardial infarction

Zhou, Xingyu; Li, Xianying; Han, Qinrong; Deng, Huijiao; Jiang, Y.; Tang, C. J.; Yang, Le

doi:10.3389/fphys.2022.991990

Cited by 5 publications

(4 citation statements)

References 56 publications

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“…In our study, ML models validated against TIMI showed an AUC value of 0.81 in a non-restricted PCI eligible population, higher than the 0.78 AUC for the fibrinolytic eligible STEMI population reported in the original TIMI study 79 . The SVM algorithm's robustness in managing high-dimensional and constrained datasets renders it ideal for predicting in-hospital mortality, and its proficiency in modelling non-linear decision boundaries is beneficial for assessing severe AMI prognosis 80 , 81 .…”

Section: Discussionmentioning

confidence: 99%

Ensemble machine learning for predicting in-hospital mortality in Asian women with ST-elevation myocardial infarction (STEMI)

Kasim,

Amir Rudin,

Malek

et al. 2024

Sci Rep

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The accurate prediction of in-hospital mortality in Asian women after ST-Elevation Myocardial Infarction (STEMI) remains a crucial issue in medical research. Existing models frequently neglect this demographic's particular attributes, resulting in poor treatment outcomes. This study aims to improve the prediction of in-hospital mortality in multi-ethnic Asian women with STEMI by employing both base and ensemble machine learning (ML) models. We centred on the development of demographic-specific models using data from the Malaysian National Cardiovascular Disease Database spanning 2006 to 2016. Through a careful iterative feature selection approach that included feature importance and sequential backward elimination, significant variables such as systolic blood pressure, Killip class, fasting blood glucose, beta-blockers, angiotensin-converting enzyme inhibitors (ACE), and oral hypoglycemic medications were identified. The findings of our study revealed that ML models with selected features outperformed the conventional Thrombolysis in Myocardial Infarction (TIMI) Risk score, with area under the curve (AUC) ranging from 0.60 to 0.93 versus TIMI's AUC of 0.81. Remarkably, our best-performing ensemble ML model was surpassed by the base ML model, support vector machine (SVM) Linear with SVM selected features (AUC: 0.93, CI: 0.89–0.98 versus AUC: 0.91, CI: 0.87–0.96). Furthermore, the women-specific model outperformed a non-gender-specific STEMI model (AUC: 0.92, CI: 0.87–0.97). Our findings demonstrate the value of women-specific ML models over standard approaches, emphasizing the importance of continued testing and validation to improve clinical care for women with STEMI.

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Section: Discussionmentioning

confidence: 99%

Ensemble machine learning for predicting in-hospital mortality in Asian women with ST-elevation myocardial infarction (STEMI)

Kasim,

Amir Rudin,

Malek

et al. 2024

Sci Rep

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“…This section reviews papers that have used ML approaches including SVM-based models to address several real-world case studies, ranging from the prediction of chronic diseases such as diabetes [105][106][107] and sleep apnea [108,109] to the diagnosis of glaucoma [110] and acute myocardial infarction [111]. The selected papers consider that the increasing prevalence of chronic diseases such as Type 2 diabetes mellitus places a heavy burden on healthcare systems.…”

Section: Classification and Prediction Problems In Real-world Case St...mentioning

confidence: 99%

“…To realise a more comprehensive understanding of the progression of patients' condition and an accurate evaluation from multiple perspectives, Zhou et al [111] considered several patients' characteristics concerning acute myocardial infarction (MI), which is one of the major causes of cardiovascular disease. They compared six ML techniques, including SVM, based on information gleaned from electronic medical records in the Medical Information Marketplace for Intensive Care (MIMIC)-III database.…”

Section: Myocardial Infarctionmentioning

confidence: 99%

An Overview on the Advancements of Support Vector Machine Models in Healthcare Applications: A Review

Guido,

Ferrisi,

Lofaro

et al. 2024

Information

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Support vector machines (SVMs) are well-known machine learning algorithms for classification and regression applications. In the healthcare domain, they have been used for a variety of tasks including diagnosis, prognosis, and prediction of disease outcomes. This review is an extensive survey on the current state-of-the-art of SVMs developed and applied in the medical field over the years. Many variants of SVM-based approaches have been developed to enhance their generalisation capabilities. We illustrate the most interesting SVM-based models that have been developed and applied in healthcare to improve performance metrics on benchmark datasets, including hybrid classification methods that combine, for instance, optimization algorithms with SVMs. We even report interesting results found in medical applications related to real-world data. Several issues around SVMs, such as selection of hyperparameters and learning from data of questionable quality, are discussed as well. The several variants developed and introduced over the years could be useful in designing new methods to improve performance in critical fields such as healthcare, where accuracy, specificity, and other metrics are crucial. Finally, current research trends and future directions are underlined.

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“…Random Forest, an ensemble method, aggregates multiple decision trees for prediction [26]. SVM seeks an optimal hyperplane for class separation, maximizing the margin [27]. KNN assigns class labels based on the majority vote among the k nearest neighbors [28].…”

Section: Model Development and Optimizationmentioning

confidence: 99%

A Machine Learning-Based Prediction of Hospital Mortality in Mechanically Ventilated ICU Patients

Li,

Ashrafi,

Kang

et al. 2024

Preprint

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BackgroundMechanical ventilation (MV) is vital for critically ill ICU patients but carries significant mortality risks. This study aims to develop a predictive model to estimate hospital mortality among MV patients, utilizing comprehensive health data to assist ICU physicians with early-stage alerts.MethodsWe developed a Machine Learning (ML) framework to predict hospital mortality in ICU patients receiving MV. Using the MIMIC-III database, we identified 25,202 eligible patients through ICD-9 codes. We employed backward elimination and the Lasso method, selecting 32 features based on clinical insights and literature. Data preprocessing included eliminating columns with over 90% missing data and using mean imputation for the remaining missing values. To address class imbalance, we used the Synthetic Minority Over-sampling Technique (SMOTE). We evaluated several ML models, including CatBoost, XGBoost, Decision Tree, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Logistic Regression, using a 70/30 train-test split. The CatBoost model was chosen for its superior performance in terms of accuracy, precision, recall, F1-score, AUROC metrics, and calibration plots.ResultsThe study involved a cohort of 25,202 patients on MV. The CatBoost model attained an AUROC of 0.862, an increase from an initial AUROC of 0.821, which was the best reported in the literature. It also demonstrated an accuracy of 0.789, an F1-score of 0.747, and better calibration, outperforming other models. These improvements are due to systematic feature selection and the robust gradient boosting architecture of CatBoost.ConclusionThe preprocessing methodology significantly reduced the number of relevant features, simplifying computational processes, and identified critical features previously overlooked. Integrating these features and tuning the parameters, our model demonstrated strong generalization to unseen data. This highlights the potential of ML as a crucial tool in ICUs, enhancing resource allocation and providing more personalized interventions for MV patients.

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Support vector machine deep mining of electronic medical records to predict the prognosis of severe acute myocardial infarction

Cited by 5 publications

References 56 publications

Ensemble machine learning for predicting in-hospital mortality in Asian women with ST-elevation myocardial infarction (STEMI)

Ensemble machine learning for predicting in-hospital mortality in Asian women with ST-elevation myocardial infarction (STEMI)

An Overview on the Advancements of Support Vector Machine Models in Healthcare Applications: A Review

A Machine Learning-Based Prediction of Hospital Mortality in Mechanically Ventilated ICU Patients

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