https://www.selleckchem.com/TGF-beta.html
958 [95%CI 0.938-0.978]), accuracy (88.9%), sensitivity (73%), negative predictive value (89%), and F1 score (80%) compared with other machine learning models. The K-nearest neighbor model generated the best specificity (99.3%) and positive predictive value (93.8%) metrics, but had low and unacceptable values for sensitivity and AUC. Most, but not all, machine learning models outperformed the existing risk prediction scores. The XGBoost model, which was generated based on a machine learning algorithm, has high potential to be used to predict ca
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