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

Algorithms used in this study. by 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. …”

Excel file with raw data that was used for all analysis shown in the paper. by Ernest L. Yufenyuy (12494849)

Interpretation of the Asante Rapid Recency Assay results is based on the presence or absence of specific lines. by Ernest L. Yufenyuy (12494849)

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

“…The classification algorithms cannot be applied on all datasets and selection of classifier is essential in this regard. …”

Image_2_Machine learning models-based on integration of next-generation sequencing testing and tumor cell sizes improve subtype classification of mature B-cell neoplasms.tif by Yafei Mu (16750227)

“…</p>Conclusions<p>The ML models were of great significance in the differential diagnosis of all cases and different MBN subtypes. Additionally, using NGS results to assist in subtype classification of MBNs by method of ML has positive clinical potential.…”

Table_1_Machine learning models-based on integration of next-generation sequencing testing and tumor cell sizes improve subtype classification of mature B-cell neoplasms.xlsx by Yafei Mu (16750227)

“…</p>Conclusions<p>The ML models were of great significance in the differential diagnosis of all cases and different MBN subtypes. Additionally, using NGS results to assist in subtype classification of MBNs by method of ML has positive clinical potential.…”

Image_1_Machine learning models-based on integration of next-generation sequencing testing and tumor cell sizes improve subtype classification of mature B-cell neoplasms.tif by Yafei Mu (16750227)

“…</p>Conclusions<p>The ML models were of great significance in the differential diagnosis of all cases and different MBN subtypes. Additionally, using NGS results to assist in subtype classification of MBNs by method of ML has positive clinical potential.…”

Graphical correlation of visual and strip reader IU values. by Ernest L. Yufenyuy (12494849)

Evaluation metrics used in this study. by 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. by 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. by 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. by 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. by 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. by 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. …”

Data_Sheet_1_Using Machine Learning to Predict the Diagnosis, Management and Severity of Pediatric Appendicitis.docx by Ricards Marcinkevics (10717293)

Image_1_A predictive model based on random forest for shoulder-hand syndrome.JPEG by Suli Yu (14947807)

“…</p>Results<p>A binary classification model was trained based on 25 handpicked features. …”

Table_1_Machine learning-based improvement of an online rheumatology referral and triage system.DOCX by Johannes Knitza (9591950)

“…The diagnostic accuracy of the current Rheport algorithm (AUROC of 0.534) could be improved with all ML models, (AUROC ranging between 0.630 and 0.737). …”

Image_4_A machine learning-based approach to predicting the malignant and metastasis of thyroid cancer.jpg by Jianhua Gu (552568)

“…The first aim is the construction of a classification model for TC based on risk factors. The second aim is the construction of a prediction model for metastasis based on risk factors.…”

Table_2_A machine learning-based approach to predicting the malignant and metastasis of thyroid cancer.docx by Jianhua Gu (552568)

“…The first aim is the construction of a classification model for TC based on risk factors. The second aim is the construction of a prediction model for metastasis based on risk factors.…”