Development and validation of machine learning models in predicting pr ...
This study aims to develop and validate machine learning models to predict LN response in breast cancer patients with LN metastases.
Rationale and Objectives This study aimed to develop and validate a machine learning -based prediction model for preoperatively predicting progesterone receptor ( PR ) expression in meningioma patients using multiparametric magnetic resonance imaging (MRI).
The ML- PR model was assessed using dicrimination, calibration curves, and decision curve analysis, and its performance was compared with existing diabetes prediction models . Based on ML- PR scores, patients were stratified into high- or low-risk categories.
Development and External Validation of a Machine Learning Model to ...
To ensure the robustness of each machine learning method, a 10-fold cross- validation resampling technique was used for the training set, and the hyperparameters were optimized using a random search until the highest area under the receiver operating characteristic curve (AUC) and accuracy of each model were achieved [16].
Patients with EBC receiving NCT followed by surgery were included in the training/ validation cohorts (TC/VC). pCR was defined as absence of residual invasive cancer on pathologic breast specimen and lymph nodes (ypT0/is ypN0). Eight ML models (c5.0, k-nearest neighbour, random forest [RF], neural network, support vector machine [linear/radial], boosted trees and boosted logistic regression ...
Development and Validation of an Interpretable Machine Learning Model ...
Risk prediction of cardiac death following percutaneous coronary intervention remains suboptimal in acute myocardial infarction. This study aimed to develop and externally validate an interpretable machine learning model using only routine laboratory and demographic variables to predict 1‐year cardiac death in this population.
Conclusion: Machine learning models can serve as reliable assessment tools for predicting the risk of preterm birth in patients with EOPE. The ensemble prediction model demonstrates the best predictive performance, helping obstetricians identify high-risk patients and perform early intervention to improve perinatal outcomes.
Evaluation of clinical prediction models (part 1)
Evaluating the performance of a clinical prediction model is crucial to establish its predictive accuracy in the populations and settings intended for use. In this article, the first in a three part series, Collins and colleagues describe the importance of a meaningful evaluation using internal, internal-external, and external validation , as well as exploring heterogeneity, fairness, and ...
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