A Comparison of LASSO Regression and Tree-Based Models for Delayed Cerebral Ischemia in Elderly Patients With Subarachnoid Hemorrhage

Hu, Ping; Liu, Yangfan; Li, Yuntao; Guo, Geng; Su, Zhongzhou; Xu, Ganggang; Chen, Junhui; Qi, Yangzhi; Xu, Yang; Yan, Tengfeng; Ye, Liguo; Sun, Qian; Deng, Gang; Chen, Qianxue

doi:10.3389/fneur.2022.791547

Cited by 8 publications

(3 citation statements)

References 38 publications

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“…Recent research has shown that machine learning algorithms outperform traditional statistical modeling approaches. The RF and LASSO methods were the most widely used machine learning methods for feature selection in most literature (27)(28)(29). Especially the LASSO method might help to solve the collinearity problem.…”

Section: Discussionmentioning

confidence: 99%

Identification of risk factors for infection after mitral valve surgery through machine learning approaches

Zhang

Fan

Ji³

et al. 2023

Front. Cardiovasc. Med.

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BackgroundSelecting features related to postoperative infection following cardiac surgery was highly valuable for effective intervention. We used machine learning methods to identify critical perioperative infection-related variables after mitral valve surgery and construct a prediction model.MethodsParticipants comprised 1223 patients who underwent cardiac valvular surgery at eight large centers in China. The ninety-one demographic and perioperative parameters were collected. Random forest (RF) and least absolute shrinkage and selection operator (LASSO) techniques were used to identify postoperative infection-related variables; the Venn diagram determined overlapping variables. The following ML methods: random forest (RF), extreme gradient boosting (XGBoost), Support Vector Machine (SVM), Gradient Boosting Decision Tree (GBDT), AdaBoost, Naive Bayesian (NB), Logistic Regression (LogicR), Neural Networks (nnet) and artificial neural network (ANN) were developed to construct the models. We constructed receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) was calculated to evaluate model performance.ResultsWe identified 47 and 35 variables with RF and LASSO, respectively. Twenty-one overlapping variables were finally selected for model construction: age, weight, hospital stay, total red blood cell (RBC) and total fresh frozen plasma (FFP) transfusions, New York Heart Association (NYHA) class, preoperative creatinine, left ventricular ejection fraction (LVEF), RBC count, platelet (PLT) count, prothrombin time, intraoperative autologous blood, total output, total input, aortic cross-clamp (ACC) time, postoperative white blood cell (WBC) count, aspartate aminotransferase (AST), alanine aminotransferase (ALT), PLT count, hemoglobin (Hb), and LVEF. The prediction models for infection after mitral valve surgery were established based on these variables, and they all showed excellent discrimination performance in the test set (AUC > 0.79).ConclusionsKey features selected by machine learning methods can accurately predict infection after mitral valve surgery, guiding physicians in taking appropriate preventive measures and diminishing the infection risk.

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Section: Discussionmentioning

confidence: 99%

Identification of risk factors for infection after mitral valve surgery through machine learning approaches

Zhang

Fan

Ji³

et al. 2023

Front. Cardiovasc. Med.

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“…To remove redundant features, reduce model over tting, and facilitate model interpretation, we used Spearman's correlation analysis (SPM; the threshold for the correlation coe cient was 0.7) and univariate analysis (p < 0.050). Least absolute shrinkage and selection operator (LASSO) [20,21]…”

Section: Radiomics Features Extraction and Selectionmentioning

confidence: 99%

Prediction model based on radiomics and clinical features for preoperative lymphovascular invasion in patients with gastric cancer with Lauren classification

Wang

Chen

Han

et al. 2022

Preprint

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Objective Lymphovascular invasion (LVI) is an independent risk factor of gastric cancer (GC) prognosis; however, LVI cannot be determined preoperatively. We explored whether a model based on contrast-enhanced computed tomography (CECT) radiomics features combined with clinicopathological factors can evaluate preoperative LVI in patients with GC with a clear Lauren classification. Methods We retrospectively analyzed 495 patients with GC, including 288 with LVI. The extracted CECT features were standardized, followed by consistency testing, correlation analysis, univariate analysis, and multivariate least absolute shrinkage and selection operator (LASSO) regression analysis. The radiomics score (Radcore) was calculated for each patient. Univariate (p < 0.10) and multivariate (p < 0.050) analyses were used to identify the clinical risk factors associated with LVI. Accordingly, three prediction models were established: Clinical + Arterial phase_Radcore, Clinical + Venous phase_Radcore, and a combined model. The prediction performances of the models were verified using receiver operating characteristics, decision curve analysis, and calibration curves in the testing dataset. The relationship between Lauren classification and LVI was analyzed using a histogram. Results The areas under the curve of the combined model were 0.8629 (95% confidence interval [CI], 0.8247–0.9011) and 0.8343 (95% CI, 0.7673–0.9012) in the training and testing datasets, respectively. The combined model had superior performance compared with the other models. Diffuse-type GC according to the Lauren classification accounted for 43.4% of LVI cases. Conclusions CECT-based radiomics models can effectively predict the preoperative LVI status in patients with GC with Lauren classification. The prediction ability of the models was effectively improved by incorporating clinicopathological factors.

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“…Subarachnoid hemorrhage caused by ruptured intracranial aneurysms (aSAH) is a global health concern due to its significant impact on mortality and long-term disability rates [ 1 ]. Approximately 35% of patients succumb to severe cerebrovascular injury in the initial weeks [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Explainable machine learning in outcome prediction of high-grade aneurysmal subarachnoid hemorrhage

Shu,

Yan,

et al. 2024

Aging

Self Cite

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Objective: Accurate prognostic prediction in patients with high-grade aneruysmal subarachnoid hemorrhage (aSAH) is essential for personalized treatment. In this study, we developed an interpretable prognostic machine learning model for high-grade aSAH patients using SHapley Additive exPlanations (SHAP). Methods: A prospective registry cohort of high-grade aSAH patients was collected in one single-center hospital. The endpoint in our study is a 12-month follow-up outcome. The dataset was divided into training and validation sets in a 7:3 ratio. Machine learning algorithms, including Logistic regression model (LR), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost), were employed to develop a prognostic prediction model for high-grade aSAH. The optimal model was selected for SHAP analysis. Results: Among the 421 patients, 204 (48.5%) exhibited poor prognosis. The RF model demonstrated superior performance compared to LR (AUC = 0.850, 95% CI: 0.783-0.918), SVM (AUC = 0.862, 95% CI: 0.799-0.926), and XGBoost (AUC = 0.850, 95% CI: 0.783-0.917) with an AUC of 0.867 (95% CI: 0.806-0 .929). Primary prognostic features identified through SHAP analysis included higher World Federation of Neurosurgical Societies (WFNS) grade, higher modified Fisher score (mFS) and advanced age, were found to be associated with 12-month unfavorable outcome, while the treatment of coiling embolization for aSAH drove the prediction towards favorable prognosis. Additionally, the SHAP force plot visualized individual prognosis predictions. Conclusions: This study demonstrated the potential of machine learning techniques in prognostic prediction for high-grade aSAH patients. The features identified through SHAP analysis enhance model interpretability and provide guidance for clinical decision-making.

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A Comparison of LASSO Regression and Tree-Based Models for Delayed Cerebral Ischemia in Elderly Patients With Subarachnoid Hemorrhage

Cited by 8 publications

References 38 publications

Identification of risk factors for infection after mitral valve surgery through machine learning approaches

Identification of risk factors for infection after mitral valve surgery through machine learning approaches

Prediction model based on radiomics and clinical features for preoperative lymphovascular invasion in patients with gastric cancer with Lauren classification

Explainable machine learning in outcome prediction of high-grade aneurysmal subarachnoid hemorrhage

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