Image 5_Developing machine learning models for predicting cardiovascular disease survival based on heavy metal serum and urine levels.tif by Hui Jin (38113)

Image 4_Developing machine learning models for predicting cardiovascular disease survival based on heavy metal serum and urine levels.tif by Hui Jin (38113)

Image 2_Developing machine learning models for predicting cardiovascular disease survival based on heavy metal serum and urine levels.tif by Hui Jin (38113)

Image 6_Developing machine learning models for predicting cardiovascular disease survival based on heavy metal serum and urine levels.tif by Hui Jin (38113)

Image 7_Developing machine learning models for predicting cardiovascular disease survival based on heavy metal serum and urine levels.tif by Hui Jin (38113)

Image 9_Developing machine learning models for predicting cardiovascular disease survival based on heavy metal serum and urine levels.tif by Hui Jin (38113)

Statistical significance tests (p-values) of Ours vs. baselines across five metrics. by Weiyan Tan (22615274)

Data Sheet 1_Construction and interpretation of tobacco leaf position discrimination model based on interpretable machine learning.docx by Ranran Kou (21785540)

“…Chemical components were analyzed for statistical significance across leaf positions, and their influence on model predictions was interpreted using SHapley Additive exPlanations (SHAP). …”

Beeswarm plot, ranked by mean absolute SHAP value generated by optimized Random Forest model. by Shimels Derso Kebede (16813901)

Subjects:

The GmNN1/FT2a-GmNFYA-C-GmENOD40 module regulates soybean nodulation. by Xinxin Li (281237)

Presentation 1_Machine learning-based mortality prediction in critically ill patients with hypertension: comparative analysis, fairness, and interpretability.pdf by Shenghan Zhang (2952879)

Data Sheet 1_Identifying emphysema risk using brominated flame retardants exposure: a machine learning predictive model based on the SHAP methodology.pdf by Qihang Xie (16490751)

Statistical significance tests (p-values) of Ours vs. ablation baselines across five metrics. by Weiyan Tan (22615274)

Comparative analysis of feature importance using the SHAP and LIME explanations on the a) CSMAR and b) MorningStar datasets. by Cai Yuanqing (22513351)

SHAP values display the sensitivity of the XGBoost model in predicting ICU mortality against different covariables. by Ahmed S. Alkhalifah (20721713)

Table 1_Development and validation of explainable machine learning models for predicting 3-month functional outcomes in acute ischemic stroke: a SHAP-based approach.docx by Cheng-fang Chen (22759622)

Surface Tension Prediction of Fuel Additives Based on Machine Learning Model with Subtraction-Average-Based Optimizer Algorithm by Jianan Wang (359744)

Subjects:

Table 1_Proteomic alterations in ovarian cancer—Predicting residual disease status using artificial intelligence and SHAP-based biomarker interpretation.docx by Seyma Yasar (18715204)

“…Introduction<p>High-grade serous ovarian cancer (HGSOC) is the most aggressive and prevalent subtype of ovarian Treatment outcomes are significantly influenced by residual disease status following neoadjuvant chemotherapy (NACT). …”

Application of Machine Learning-Based Models to Understand and Predict Critical Flux of Oil-in-Water Emulsion in Crossflow Microfiltration by Henry J. Tanudjaja (12106715)

Subjects:

Supplementary file 1_Identifying gastric intestinal metaplasia risk based on clinical indicators: a machine learning predictive model based on the SHAP methodology.xls by Yufen Wang (827997)

“…Additionally, the application of SHAP values improves the understanding of how features contribute to predictions, while the newly developed web-based calculator offers a practical tool for clinicians to evaluate GIM risk more easily.…”