A cluster-based ensemble approach for congenital heart disease prediction

Kaur, Ishleen; Ahmad, Tanvir

doi:10.1016/j.cmpb.2023.107922

Cited by 1 publication

(1 citation statement)

References 30 publications

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“…This algorithm is straightforward and relies on the classification of new data points based on their k‐nearest neighbors. 25 Notably, it is a nonparametric and lazy algorithm as it does not make assumptions regarding the underlying data distribution during training.…”

Section: Methodsmentioning

confidence: 99%

Development and Validation of a Machine Learning Algorithm to Predict the Risk of Blood Transfusion after Total Hip Replacement in Patients with Femoral Neck Fractures: A Multicenter Retrospective Cohort Study

Zhu,

Xu,

Jiang

et al. 2024

Orthopaedic Surgery

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ObjectiveTotal hip arthroplasty (THA) remains the primary treatment option for femoral neck fractures in elderly patients. This study aims to explore the risk factors associated with allogeneic blood transfusion after surgery and to develop a dynamic prediction model to predict post‐operative blood transfusion requirements. This will provide more accurate guidance for perioperative humoral management and rational allocation of medical resources.MethodsWe retrospectively analyzed data from 829 patients who underwent total hip arthroplasty for femoral neck fractures at three third‐class hospitals between January 2017 and August 2023. Patient data from one hospital were used for model development, whereas data from the other two hospitals were used for external validation. Logistic regression analysis was used to screen the characteristic subsets related to blood transfusion. Various machine learning algorithms, including logistic regression, SVA (support vector machine), K‐NN (k‐nearest neighbors), MLP (multilayer perceptron), naive Bayes, decision tree, random forest, and gradient boosting, were used to process the data and construct prediction models. A 10‐fold cross‐validation algorithm facilitated the comparison of the predictive performance of the models, resulting in the selection of the best‐performing model for the development of an open‐source computing program.ResultsBMI (body mass index), surgical duration, IBL (intraoperative blood loss), anticoagulant history, utilization rate of tranexamic acid, Pre‐Hb, and Pre‐ALB were included in the model as well as independent risk factors. The average area under curve (AUC) values for each model were as follows: logistic regression (0.98); SVA (0.91); k‐NN (0.87) MLP, (0.96); naive Bayes (0.97); decision tree (0.87); random forest (0.96); and gradient boosting (0.97). A web calculator based on the best model is available at: (https://nomo99.shinyapps.io/dynnomapp/).ConclusionUtilizing a computer algorithm, a prediction model with a high discrimination accuracy (AUC > 0.5) was developed. The logistic regression model demonstrated superior differentiation and reliability, thereby successfully passing external validation. The model's strong generalizability and applicability have significant implications for clinicians, aiding in the identification of patients at high risk for postoperative blood transfusion.

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Section: Methodsmentioning

confidence: 99%