Table 1_A machine learning approach to predict postoperative sleep disturbance after total knee arthroplasty: a comparative study of multiple algorithms.docx by Yi-xiang Zhang (22563236)

Results of subgroup analysis. by Liyu Lin (5760167)

“…Among the seven machine learning predictive models incorporating NHHR, the XGBoost algorithm exhibited the highest predictive performance, with an area under the curve (AUC) of 0.828. …”

Information of the included study population. by Liyu Lin (5760167)

“…Among the seven machine learning predictive models incorporating NHHR, the XGBoost algorithm exhibited the highest predictive performance, with an area under the curve (AUC) of 0.828. …”

The association between NHHR and CAP. by Liyu Lin (5760167)

“…Among the seven machine learning predictive models incorporating NHHR, the XGBoost algorithm exhibited the highest predictive performance, with an area under the curve (AUC) of 0.828. …”

The creation of last three bills. by Misbah Liaqat (3134061)

“…Blockchain is a decentralized technology that employs secure hashing and consensus algorithms that can detect any data modification. Hence, this work proposes a blockchain-enabled immutable, and efficient framework for trade documentation in oil port logistics. …”

Resource metrics of the proposed framework. by Misbah Liaqat (3134061)

“…Blockchain is a decentralized technology that employs secure hashing and consensus algorithms that can detect any data modification. Hence, this work proposes a blockchain-enabled immutable, and efficient framework for trade documentation in oil port logistics. …”

Layered architecture of the proposed framework. by Misbah Liaqat (3134061)

“…Blockchain is a decentralized technology that employs secure hashing and consensus algorithms that can detect any data modification. Hence, this work proposes a blockchain-enabled immutable, and efficient framework for trade documentation in oil port logistics. …”

Installation of chaincode on the peer node. by Misbah Liaqat (3134061)

“…Blockchain is a decentralized technology that employs secure hashing and consensus algorithms that can detect any data modification. Hence, this work proposes a blockchain-enabled immutable, and efficient framework for trade documentation in oil port logistics. …”

Related work summary with their limitations. by Misbah Liaqat (3134061)

“…Blockchain is a decentralized technology that employs secure hashing and consensus algorithms that can detect any data modification. Hence, this work proposes a blockchain-enabled immutable, and efficient framework for trade documentation in oil port logistics. …”

Calculating the TAMs-associated score. by Changquan Shang (21647625)

Data Sheet 1_An individualized risk prediction tool for ectopic pregnancy within the first 10 weeks of gestation based on machine learning algorithms.docx by Xin Du (208780)

“…</p>Conclusion<p>This study employed the CatBoost algorithm to develop an individualized risk prediction model by integrating multiple features from the initial visit. …”

Table 1_Combinations of multimodal neuroimaging biomarkers and cognitive test scores to identify patients with cognitive impairment.docx by Yuriko Nakaoku (17874681)

“…Finally, MCI identification models were developed using a penalized logistic regression model with an elastic net algorithm.…”

Supplementary Material 8 by Nishitha R Kumar (19750617)

“…</li><li><b>Adaboost: </b>A boosting algorithm that combines weak classifiers iteratively, refining predictions and improving the identification of antimicrobial resistance patterns.…”

Supplementary file 1_Machine learning identifies immune-perinatal predictors of infantile hemangioma.xlsx by Dongdong Wu (824557)

“…Candidate risk factors were identified using logistic regression. Four machine learning algorithms—XGBoost, Random Forest, Support Vector Machine, and k-Nearest Neighbors—were employed to construct predictive models. …”

Supplementary file 2_Machine learning identifies immune-perinatal predictors of infantile hemangioma.xlsx by Dongdong Wu (824557)

“…Candidate risk factors were identified using logistic regression. Four machine learning algorithms—XGBoost, Random Forest, Support Vector Machine, and k-Nearest Neighbors—were employed to construct predictive models. …”

Data Sheet 1_A machine-learning approach for pancreatic neoplasia classification based on plasma extracellular vesicles.pdf by Ioanna Angelioudaki (21177620)

“…Various ML methods were applied, including Logistic Regression, Random Forest, Support Vector Machines, and Extreme Gradient Boosting. …”

Data Sheet 1_Retrospective BReast Intravoxel Incoherent Motion Multisite (BRIMM) multisoftware study.pdf by Dibash Basukala (20772110)

“…Pearson correlation (r) coefficients between software pairs were computed while logistic regression model was implemented to test malignancy detection and assess robustness of the IVIM metrics.…”

Image 2_Retrospective BReast Intravoxel Incoherent Motion Multisite (BRIMM) multisoftware study.jpeg by Dibash Basukala (20772110)

“…Pearson correlation (r) coefficients between software pairs were computed while logistic regression model was implemented to test malignancy detection and assess robustness of the IVIM metrics.…”

Image 4_Retrospective BReast Intravoxel Incoherent Motion Multisite (BRIMM) multisoftware study.jpeg by Dibash Basukala (20772110)

“…Pearson correlation (r) coefficients between software pairs were computed while logistic regression model was implemented to test malignancy detection and assess robustness of the IVIM metrics.…”

Image 3_Retrospective BReast Intravoxel Incoherent Motion Multisite (BRIMM) multisoftware study.jpeg by Dibash Basukala (20772110)

“…Pearson correlation (r) coefficients between software pairs were computed while logistic regression model was implemented to test malignancy detection and assess robustness of the IVIM metrics.…”