{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["2023"],"submitter":["Islam R"],"funding":["American International University-Bangladesh"],"pubmed_abstract":["Diabetes is one of the most serious chronic diseases that result in high blood sugar levels. Early prediction can significantly diminish the potential jeopardy and severity of diabetes. In this study, different machine learning (ML) algorithms were applied to predict whether an unknown sample had diabetes or not. However, the main significance of this research was to provide a clinical decision support system (CDSS) by predicting type 2 diabetes using different ML algorithms. For the research purpose, the publicly available Pima Indian Diabetes (PID) dataset was used. Data preprocessing, K-fold cross-validation, hyperparameter tuning, and various ML classifiers such as K-nearest neighbor (KNN), decision tree (DT), random forest (RF), Naïve Bayes (NB), support vector machine (SVM), and hist"],"journal":["Journal of healthcare engineering"],"pagination":["6992441"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10243956"],"repository":["biostudies-literature"],"pubmed_title":["Clinical Decision Support System for Diabetic Patients by Predicting Type 2 Diabetes Using Machine Learning Algorithms."],"pmcid":["PMC10243956"],"pubmed_authors":["Islam MR","Islam R","Tuhin MN","Saikat MSH","Sultana A"],"additional_accession":[]},"is_claimable":false,"name":"Clinical Decision Support System for Diabetic Patients by Predicting Type 2 Diabetes Using Machine Learning Algorithms.","description":"Diabetes is one of the most serious chronic diseases that result in high blood sugar levels. Early prediction can significantly diminish the potential jeopardy and severity of diabetes. In this study, different machine learning (ML) algorithms were applied to predict whether an unknown sample had diabetes or not. However, the main significance of this research was to provide a clinical decision support system (CDSS) by predicting type 2 diabetes using different ML algorithms. For the research purpose, the publicly available Pima Indian Diabetes (PID) dataset was used. Data preprocessing, K-fold cross-validation, hyperparameter tuning, and various ML classifiers such as K-nearest neighbor (KNN), decision tree (DT), random forest (RF), Naïve Bayes (NB), support vector machine (SVM), and hist","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023","modification":"2026-06-03T15:16:41.601Z","creation":"2025-04-19T07:22:04.329Z"},"accession":"S-EPMC10243956","cross_references":{"pubmed":["37287539"],"doi":["10.1155/2023/6992441"]}}