Image_1_Application of Machine Learning Algorithms to Predict Central Lymph Node Metastasis in T1-T2, Non-invasive, and Clinically Node Negative Papillary Thyroid Carcinoma.TIFF by Jiang Zhu (40858)

Data_Sheet_1_Explainable ensemble machine learning model for prediction of 28-day mortality risk in patients with sepsis-associated acute kidney injury.doc by Jijun Yang (5119103)

Table_1_Machine learning models for assessing risk factors affecting health care costs: 12-month exercise-based cardiac rehabilitation.DOCX by Arto J. Hautala (3072576)

“…ECR was programmed in accordance with international guidelines. Risk analysis algorithms (cross-decomposition algorithms) were employed to rank risk factors based on variances in their effects. …”

Table 1_Explainable machine learning-based prediction of early and mid-term postoperative complications in adolescent tibial fractures.docx by Yufeng Wang (274657)

“…Conventional coagulation markers (PT/APTT/FIB) lack combinatorial risk assessment. We developed an explainable ML model integrating coagulation and clinical features to predict adverse events.…”

Comparison of the four models. by Gaosha Li (20570760)

“…A combination of four machine learning algorithms (XGBoost、Logistic Regression、Random Forest、AdaBoost) was employed to predict NPM recurrence, and the model with the highest Area Under the Curve (AUC) in the test set was selected as the best model. …”

Table_1_Data-based modeling for hypoglycemia prediction: Importance, trends, and implications for clinical practice.docx by Liyin Zhang (6371999)

Table_1_Prediction-Driven Decision Support for Patients With Mild Stroke: A Model Based on Machine Learning Algorithms.xlsx by Xinping Lin (1591438)

“…The optimal ML model was selected to be DAMS. In addition, SHapley Additive exPlanations (SHAP) approach was introduced to rank the feature importance. …”

Dendrogram of the stock prices. by Muhammad Hilal Alkhudaydi (21560690)

“…This article uses feature-based models to investigate the primary elements that contribute to the optimal composition of a specific portfolio. …”

Descriptive statistics on stock prices. by Muhammad Hilal Alkhudaydi (21560690)

“…This article uses feature-based models to investigate the primary elements that contribute to the optimal composition of a specific portfolio. …”

Correlation heatmap of the principal components. by Muhammad Hilal Alkhudaydi (21560690)

“…This article uses feature-based models to investigate the primary elements that contribute to the optimal composition of a specific portfolio. …”

Data_Sheet_1_Prediction-Driven Decision Support for Patients With Mild Stroke: A Model Based on Machine Learning Algorithms.docx by Xinping Lin (1591438)

“…The optimal ML model was selected to be DAMS. In addition, SHapley Additive exPlanations (SHAP) approach was introduced to rank the feature importance. …”

Set of variables VS model performance. by Gaosha Li (20570760)

“…A combination of four machine learning algorithms (XGBoost、Logistic Regression、Random Forest、AdaBoost) was employed to predict NPM recurrence, and the model with the highest Area Under the Curve (AUC) in the test set was selected as the best model. …”

Data_Sheet_1_Optimizing Spatio-Temporal Allocation of the COVID-19 Vaccine Under Different Epidemiological Landscapes.pdf by Wen Cao (696121)

Data_Sheet_1_Risk factors of lymph node metastasis or lymphovascular invasion for superficial esophageal squamous cell carcinoma: A practical and effective predictive nomogram base... by Yali Tao (10772160)

Table 1_Assessing accuracy of BiliPredics algorithm in predicting individual bilirubin progression in neonates—results from a prospective multi-center study.docx by Britta Steffens (13231326)

Cluster Analysis of Cardiovascular Phenotypes in Patients With Type 2 Diabetes and Established Atherosclerotic Cardiovascular Disease: A Potential Approach to Precision Medicine by Abhinav Sharma (672274)

“…Cluster analysis using 40 baseline variables was conducted, with associations between clusters and the primary composite outcome (cardiovascular death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for unstable angina) assessed by Cox proportional hazards models. …”

Table_1_Construction and validation of nomograms combined with novel machine learning algorithms to predict early death of patients with metastatic colorectal cancer.docx by Yalong Zhang (711724)

Image_2_Construction and validation of nomograms combined with novel machine learning algorithms to predict early death of patients with metastatic colorectal cancer.TIF by Yalong Zhang (711724)

Image_3_Construction and validation of nomograms combined with novel machine learning algorithms to predict early death of patients with metastatic colorectal cancer.TIF by Yalong Zhang (711724)

Image_1_Construction and validation of nomograms combined with novel machine learning algorithms to predict early death of patients with metastatic colorectal cancer.TIF by Yalong Zhang (711724)