2026-10-11T03:52:29Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33813617 2026-09-15T17:51:46Z category_13 category_734 category_931 category_811 portal_5 item_type_3 month_year_09_2026

<p>Performance of various machine learning models on diabetes prediction datasets. This table shows accuracy and whether explainable AI techniques (XAI) were used for each referenced model.</p> Saher Fatima Awan (24949757) Kainat Irfan (24949760) Umair Muneer Butt (13751570) Sukumar Letchmunan (13751573) Fadratul Hafinaz Hassan (11422834) Genetics Biological Sciences not elsewhere classified Information Systems not elsewhere classified Mathematical Sciences not elsewhere classified uses random forest support vector machine shapley additive explanation received limited attention influential features contributing included standard scaling based feature selection anova )- f xlink "> diabetes stacked ensemble model local interpretable model improving prediction accuracy predicting diabetes trustworthy decision timely interventions thus supporting smote ). significantly increases serious complications preprocessing pipeline predictive modeling main focus latest methods kidney failure gradient boosting enhance transparency early detection class balancing chronic disease cardiovascular disorders base learners agnostic explanations 86 %. <p>Performance of various machine learning models on diabetes prediction datasets. This table shows accuracy and whether explainable AI techniques (XAI) were used for each referenced model.</p> 2026-09-15T17:51:29Z Dataset Dataset 10.1371/journal.pone.0352313.t001 https://figshare.com/articles/dataset/_p_Performance_of_various_machine_learning_models_on_diabetes_prediction_datasets_This_table_shows_accuracy_and_whether_explainable_AI_techniques_XAI_were_used_for_each_referenced_model_p_/33813617 CC BY 4.0