International Journal of Endocrinology and Metabolism
Research Institute for Endocrine Sciences
Scopus by Title (Ref)
Articles that cite the referenced work, matched by title from the Scopus database.
Analysis of pediatric absence epilepsy electroencephalograms using a mining-based association rule approach: A cross-sectional study based on realistic data
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Discovering internal medicine examinations using apriori association rule mining
Gumushane Universitesi Fen Bilimleri Dergisi
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Association rule mining of time-based patterns in diabetes-related comorbidities on imbalanced data: a pre- and post-diagnosis study
BMC Medical Informatics and Decision Making
December 2025
Bata R.
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Machine and deep learning techniques for the prediction of diabetics: a review
Multimedia Tools and Applications
May 2025
Modak S.K.S.
Association Rule Mining-based Analysis of Clinical Manifestations of SARS-Cov-2 (COVID-19) Coronavirus Infection in Senegal
ACM International Conference Proceeding Series
5 April 2025
Diallo A.
Open Access
Assessing body composition through anthropometry: Implications for diagnosing and managing polycystic ovary syndrome (PCOS)
Clinical Physiology and Functional Imaging
January 2025
Parua S.
The diabetes mellitus multimorbidity network in hospitalized patients over 50 years of age in China: data mining of medical records
BMC Public Health
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Chen C.
Open Access
Exploring the Intersection of Machine Learning and Causality in Advanced Diabetes Management: New Insight and Opportunities
Synthesis Lectures on Engineering Science and Technology
2024
Echajei S.
Enhancing Cardiovascular Disease Analytics with Demographic and Behavioral Risk Features through Unsupervised and Ensemble Learning Techniques
Proceedings of the IEEE Conference on Systems Process and Control Icspc
2024
Sem L.X.
Quality improvement of healthcare services through data analytics processes
Advancement in Business Analytics Tools for Higher Financial Performance
8 August 2023
Damali U.
Data-Driven Analysis of Stimulation Treatments Using Association Rule Mining
SPE Production and Operations
2023
Ahmadi R.
All Open Access, Green
Research on Optimization of Diabetes Diagnosis Model Based on Data Mining
Proceedings 2023 International Seminar on Computer Science and Engineering Technology Scset 2023
2023
Li Z.
Discovering the symptom patterns of COVID-19 from recovered and deceased patients using Apriori association rule mining
Informatics in Medicine Unlocked
January 2023
Dehghani M.
Open Access
Environmental exposures in machine learning and data mining approaches to diabetes etiology: A scoping review
Artificial Intelligence in Medicine
January 2023
Mistry S.
Open Access
An effective text mining framework using adaptive principle component analysis
Multimedia Tools and Applications
December 2022
Kala K.
Associations Among Multimorbid Conditions in Hospitalized Middle-aged and Older Adults in China: Statistical Analysis of Medical Records
Jmir Public Health and Surveillance
November 2022
Zhang Y.
Open Access
Relevance of Frequent Pattern (FP)-Growth-Based Association Rules on Liver Diseases
Lecture Notes in Networks and Systems
2022
Patel P.
Using Data Mining Techniques for Designing Patient-Friendly Hospitals
Contributions to Economics
2022
Kocakoç İ.D.
Mining associations between glycemic variability in awake-time and in-sleep among non-diabetic adults
Frontiers in Medical Technology
2022
Liang Z.
Open Access
A survey on data mining techniques used in medicine
Journal of Diabetes and Metabolic Disorders
December 2021
Birjandi S.M.
All Open Access, Green
Association rules mining for hospital readmission: A case study
Mathematics
November-1 2021
Miswan N.H.
Open Access
Review on Text Mining of Electronic Medical Record
Jisuanji Yanjiu Yu Fazhan Computer Research and Development
March 2021
Wu Z.
Reconstruction Error-Based Efficient Deep Neural Network for Diabetes Prediction in the Korean Population
Smart Innovation Systems and Technologies
2021
Amarbayasgalan T.
Detection and Prediction of Diabetes Using Data Mining: A Comprehensive Review
IEEE Access
2021
Khan F.A.
Open Access