The diagnosis of diabetes mellitus utilizing clinical data and vital signs is essential for the early identification and efficient management of the condition, particularly as its global prevalence rises. This study examines general diagnostic methodologies utilizing diverse indicators, with a particular emphasis on diabetes diagnostics, subsequently identifying pertinent features to develop an effective, cohesive diagnostic solution. The suggested solution uses both structured and unstructured features. Structured features include vital signs, demographics, and lab tests, while unstructured features include chief complaints and medical notes. This solution is based on the MIMIC-IV dataset, which is a rich and varied source of medical data. The research suggests creating a prototype diagnostic system that uses modern machine learning models like Logistic Regression, Support Vector Machine (SVM), Gradient Boosting, Random Forest, and XGBoost. This system is built to automatically process and combine information from different types of data, such as medical text and biological indicators, to improve the prediction process and help doctors make decisions. This solution not only offers a thorough and efficient method for diagnosing diabetes, but it also shows how it could be used in real-world healthcare systems. The prototype system can be used to help doctors make quick and correct clinical decisions. It can also be used as a starting point for future research and applications in smart healthcare.
Research Article | Open Access | Download Full Text
Volume 4 | Issue 3 | Year 2025 | Article Id: DST-V4I3P101 DOI: https://doi.org/10.59232/DST-V4I3P101
A Machine Learning-Based Solution for Diabetes Diagnosis: Integrating Clinical Data and Vital Signs
Huy Huynh, Thanh Cao, Hai Tran
| Received | Revised | Accepted | Published |
|---|---|---|---|
| 18 Jul 2025 | 10 Aug 2025 | 18 Sep 2025 | 28 Sep 2025 |
Citation
Huy Huynh, Thanh Cao, Hai Tran. “A Machine Learning-Based Solution for Diabetes Diagnosis: Integrating Clinical Data and Vital Signs.” DS Journal of Digital Science and Technology, vol. 4, no. 3, pp. 1-15, 2025.
Abstract
Keywords
Diabetes Diagnosis, Machine Learning, Clinical Information, Vital Signs, MIMIC-IV Dataset.
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