Summary of all non-clinical data (laboratory and others) presented in the data set used by the primary studies. حسب Sebastião Rogério da Silva Neto (11944944)

Algorithms used in this study. حسب Daniel Sanchez-Gomez (19065975)

"…This allowed us to develop a machine learning-based framework for the prediction of bead-forming minerals by training and benchmarking 13 of the most widely used supervised algorithms. …"

Class system distribution. حسب Daniel Sanchez-Gomez (19065975)

"…This allowed us to develop a machine learning-based framework for the prediction of bead-forming minerals by training and benchmarking 13 of the most widely used supervised algorithms. …"

Hi-C read-pair classifications. حسب Matthew Z. DeMaere (6404834)

Learning curve for the three carbon sources. حسب Manish Pant (13932201)

"…The attributes were subjected to comparative classification on various classifiers and based on accuracy, multilayer perceptron (neural network algorithm) was selected as classifier. …"

Best model class-wise performance on SMIDS. حسب Şafak Kılıç (22227019)

"…The framework incorporates multiple feature extraction layers (CBAM, GAP, GMP, pre-final) combined with 10 distinct feature selection methods including Principal Component Analysis (PCA), Chi-square test, Random Forest importance, variance thresholding, and their intersections. Classification is performed using Support Vector Machines with RBF/Linear kernels and k-Nearest Neighbors algorithms. …"

Best model class-wise performance on HuSHeM. حسب Şafak Kılıç (22227019)

"…The framework incorporates multiple feature extraction layers (CBAM, GAP, GMP, pre-final) combined with 10 distinct feature selection methods including Principal Component Analysis (PCA), Chi-square test, Random Forest importance, variance thresholding, and their intersections. Classification is performed using Support Vector Machines with RBF/Linear kernels and k-Nearest Neighbors algorithms. …"

Evaluation metrics used in this study. حسب Daniel Sanchez-Gomez (19065975)

"…This allowed us to develop a machine learning-based framework for the prediction of bead-forming minerals by training and benchmarking 13 of the most widely used supervised algorithms. …"

Model benchmark. حسب Daniel Sanchez-Gomez (19065975)

"…This allowed us to develop a machine learning-based framework for the prediction of bead-forming minerals by training and benchmarking 13 of the most widely used supervised algorithms. …"

Model performance before and after optimisation. حسب Daniel Sanchez-Gomez (19065975)

"…This allowed us to develop a machine learning-based framework for the prediction of bead-forming minerals by training and benchmarking 13 of the most widely used supervised algorithms. …"

Proof-of-concept confusion matrix. حسب Daniel Sanchez-Gomez (19065975)

"…This allowed us to develop a machine learning-based framework for the prediction of bead-forming minerals by training and benchmarking 13 of the most widely used supervised algorithms. …"

Evaluation metrics for VS1 and VS2. حسب Daniel Sanchez-Gomez (19065975)

"…This allowed us to develop a machine learning-based framework for the prediction of bead-forming minerals by training and benchmarking 13 of the most widely used supervised algorithms. …"

Average accuracy by feature extraction layer. حسب Şafak Kılıç (22227019)

"…The framework incorporates multiple feature extraction layers (CBAM, GAP, GMP, pre-final) combined with 10 distinct feature selection methods including Principal Component Analysis (PCA), Chi-square test, Random Forest importance, variance thresholding, and their intersections. Classification is performed using Support Vector Machines with RBF/Linear kernels and k-Nearest Neighbors algorithms. …"

Performance analysis by feature extraction layer. حسب Şafak Kılıç (22227019)

"…The framework incorporates multiple feature extraction layers (CBAM, GAP, GMP, pre-final) combined with 10 distinct feature selection methods including Principal Component Analysis (PCA), Chi-square test, Random Forest importance, variance thresholding, and their intersections. Classification is performed using Support Vector Machines with RBF/Linear kernels and k-Nearest Neighbors algorithms. …"

Average accuracy by feature selection method. حسب Şafak Kılıç (22227019)

"…The framework incorporates multiple feature extraction layers (CBAM, GAP, GMP, pre-final) combined with 10 distinct feature selection methods including Principal Component Analysis (PCA), Chi-square test, Random Forest importance, variance thresholding, and their intersections. Classification is performed using Support Vector Machines with RBF/Linear kernels and k-Nearest Neighbors algorithms. …"

Classifier performance overview. حسب Şafak Kılıç (22227019)

"…The framework incorporates multiple feature extraction layers (CBAM, GAP, GMP, pre-final) combined with 10 distinct feature selection methods including Principal Component Analysis (PCA), Chi-square test, Random Forest importance, variance thresholding, and their intersections. Classification is performed using Support Vector Machines with RBF/Linear kernels and k-Nearest Neighbors algorithms. …"

Data Sheet 1_Real-world data-driven early warning system for risk-stratified liver injury in hospitalized COVID-19 patients—Machine learning models for clinical decision support.do... حسب Yuanguo Xiong (20135991)

"…The online prediction platforms were developed for liver injury early warning risk stratification (low- and high-risk) based on predicted probabilities classification.</p>Conclusion<p>This research successfully established a machine learning-powered early warning system capable of real-time risk stratification for COVID-19-associated liver injury through dynamic integration of clinical data. …"