Model construction and validation (A) Construction of the model based on multiple machine learning algorithms. by Chengting Wu (21485612)

ROC curve comparison for different machine learning algorithms. by Shaik Ahamed Fayaz (19859670)

Subjects:

Histogram of accuracy values for different machine learning models across multiple runs. by Amal H. Alharbi (21755906)

rbpTransformer model using multiple cores. by Ahmet Gürhanlı (21602042)

Data Sheet 1_Predictive model establishment for forward-head posture disorder in primary-school-aged children based on multiple machine learning algorithms.csv by Hongjun Tao (21448853)

“…Multiple machine learning algorithms are applied to construct distinct risk prediction models, with the most effective model selected through comparative analysis. …”

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)

Machine learning hyperparameters used for this study. by Shaik Ahamed Fayaz (19859670)

Subjects:

The impact of step sizes on pressure prediction in fracturing treatment via deep learning algorithms by Yuxuan Liu (349237)

Equitable Hospital Length of Stay Prediction for Patients with Learning Disabilities and Multiple Long-term Conditions Using Machine Learning by Emeka Abakasanga (14235503)

“…Predicting the length of stay (LOS) for patients with LD and multiple long-term conditions (MLTCs) is critical for improving patient care and optimising medical resource allocation. …”

Crop yield prediction accuracy using different ML algorithms. by Gourab Saha (8987405)

Subjects:

Data_Sheet_1_Development of prognostic models for advanced multiple hepatocellular carcinoma based on Cox regression, deep learning and machine learning algorithms.CSV by Jie Shen (31533)

“…</p>Methods<p>Eligible patients with HCC were obtained from the Surveillance, Epidemiology, and End Results (SEER) database, and then prognostic models were built using Cox regression, machine learning (ML), and deep learning (DL) algorithms. The model’s performance was evaluated using C-index, receiver operating characteristic curve, Brier score and decision curve analysis, respectively, and the best model was interpreted using SHapley additive explanations (SHAP) interpretability technique.…”

Data_Sheet_2_Development of prognostic models for advanced multiple hepatocellular carcinoma based on Cox regression, deep learning and machine learning algorithms.docx by Jie Shen (31533)

“…</p>Methods<p>Eligible patients with HCC were obtained from the Surveillance, Epidemiology, and End Results (SEER) database, and then prognostic models were built using Cox regression, machine learning (ML), and deep learning (DL) algorithms. The model’s performance was evaluated using C-index, receiver operating characteristic curve, Brier score and decision curve analysis, respectively, and the best model was interpreted using SHapley additive explanations (SHAP) interpretability technique.…”

Image 2_SHKBP1 is a target for sepsis: evidence from WGCNA and multiple machine learning algorithms.tif by Fan Wu (43375)

Image 3_SHKBP1 is a target for sepsis: evidence from WGCNA and multiple machine learning algorithms.tif by Fan Wu (43375)

Image 4_SHKBP1 is a target for sepsis: evidence from WGCNA and multiple machine learning algorithms.tif by Fan Wu (43375)

Image 1_SHKBP1 is a target for sepsis: evidence from WGCNA and multiple machine learning algorithms.tif by Fan Wu (43375)

Image 5_SHKBP1 is a target for sepsis: evidence from WGCNA and multiple machine learning algorithms.tif by Fan Wu (43375)

Table 1_Using machine learning to predict the rupture risk of multiple intracranial aneurysms.xlsx by Junqiang Feng (10300150)

“…The widely used PHASES score does not incorporate morphological parameters of aneurysms and is not specifically designed for patients with multiple aneurysms. Therefore, we constructed a risk prediction model for the rupture of MIAs by machine learning algorithms.…”

DataSheet1_Development of a COVID-19 early risk assessment system based on multiple machine learning algorithms and routine blood tests: a real-world study.docx by Qiangqiang Qin (12533191)

Mitochondrial toxic prediction of marine alga toxins using a predictive model based on feature coupling and ensemble learning algorithms by Guangyin Jia (21075537)

“…By comparing 8 machine learning algorithms and using a weighted soft voting method to integrate the two optimal algorithms, we established 108 prediction models and identified the best ensemble learning model MACCS_LK for screening and defining its application domain. …”