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lupus.bmj7d
Prediction of mortality risk in critically ill patients with systemic lupus erythematosus: a machine learning approach using the MIMIC-IV database
Objective Early prediction of long-term outcomes in patients with systemic lupus erythematosus (SLE) remains a great challenge in clinical practice. Our study aims to develop and validate predictive ...
C&EN13d
XGBoost: An Optimal Machine Learning Model with Just Structural Features to Discover MOF Adsorbents of Xe/Kr | ACS Omega - ACS Publications
The inert gases Xe and Kr mainly exist in the used nuclear fuel (UNF) with the Xe/Kr ratio of 20:80, which it is difficult to separate. In this work, based on the G-MOFs database, high-throughput ...
GitHub19d
xgboost-classifier · GitHub Topics · GitHub
A machine learning project for binary classification of skin cancer as malignant or benign, utilizing models like XGBoost, LGBM Classifier, Adaboost, SVM, and Logistic Regression. Features ...
IEEE22d
Heart failure survival prediction using machine learning algorithm: am I safe from heart failure? | IEEE Conference Publication | IEEE Xplore
Our prediction models used the following machine learning techniques- Logistic Regression, Decision Tree, Support Vector Machine, XGBoost, LightGBM, Random Forest, KNN, and Bagging and were able to ...
Frontiers25d
Machine learning-based prediction of 6-month functional recovery in hypertensive cerebral hemorrhage: insights from XGBoost and SHAP analysis - Frontiers
In this study, we compared multiple machine learning algorithms and demonstrated, for the first time, the significant advantage of the XGBoost model in predicting 6-month HICH prognosis (AUC = 0.921 ...