Machine Learning Based Early Stage Diabetes Detection System
Abstract & Details
Research Area
Computer Engineering
Keywords
Machine Learning
Early Stage Diabetes Detection
Predictive Analytics
Health Informatics
Classification Algorithms
Explainable AI (XAI)
Web Application
Medical Diagnosis
Preventive Healthcare
Abstract
Early diabetes diagnosis stays hard because illnesses linked to daily habits grow common and classic tests reach clear limits. The Machine Learning-Based Early Stage Diabetes Detection System supplies a plain, smart guess of diabetes risk after it checks clinical plus lifestyle facts. The system splits into three modules - a front end that gathers data, a middle layer that runs prediction models and a back layer that keeps each record in a secure database. It trains supervised models - Random Forest, XGBoost besides Support Vector Machine - to tag each patient as diabetic or non-diabetic. To clarify the results, the model adds Explainable AI (XAI) tools like SHAP to highlight the features that shape each prediction. As a web-based platform, the system enables both patients and healthcare providers to conduct real-time risk evaluations, access analytical reports, and make well-informed decisions. By prioritizing accuracy, transparency, and user-friendliness, this study outlines the methodology, architecture, and proposed algorithms for creating an effective, interpretable, and accessible solution for early diabetes detection.
License
This work is licensed under a Creative
Commons
Attribution-ShareAlike 4.0 International License.
Author Information
| # | Name | Institute / Affiliation |
|---|---|---|
| 1 | Rohan Mulik | D Y Patil College of Engineering Akurdi |
| 2 | Vidit Singh | D Y Patil College of Engineering Akurdi |
| 3 | Viraj Patel | D Y Patil College of Engineering Akurdi |
| 4 | Pranav Dongare | D Y Patil College of Engineering Akurdi |
| 5 | Deepali Gohil | D Y Patil College of Engineering Akurdi |
| 6 | Radhika Gore | D Y Patil College of Engineering Akurdi |
How to Cite
Use the following formats to cite this article in your research.
APA Style
Mulik, Rohan, Singh, Vidit, Patel, Viraj, Dongare, Pranav, Gohil, Deepali, & Gore, Radhika (2026). Machine Learning Based Early Stage Diabetes Detection System. International Journal of Advance Research and Innovative Ideas In Education, 12(2), 1739-1744.
MLA Style
Mulik, Rohan, et al. "Machine Learning Based Early Stage Diabetes Detection System." International Journal of Advance Research and Innovative Ideas In Education, vol. 12, no. 2, 2026, pp. 1739-1744.
IEEE Style
Rohan Mulik, Vidit Singh, Viraj Patel, Pranav Dongare, Deepali Gohil, and Radhika Gore, "Machine Learning Based Early Stage Diabetes Detection System," International Journal of Advance Research and Innovative Ideas In Education, vol. 12, no. 2, pp. 1739-1744, 2026.
Vancouver Style
Mulik Rohan, Singh Vidit, Patel Viraj, Dongare Pranav, Gohil Deepali, Gore Radhika. Machine Learning Based Early Stage Diabetes Detection System. International Journal of Advance Research and Innovative Ideas In Education. 2026;12(2):1739-1744.
Harvard Style
Mulik, Rohan, Singh, Vidit, Patel, Viraj, Dongare, Pranav, Gohil, Deepali, & Gore, Radhika (2026) 'Machine Learning Based Early Stage Diabetes Detection System', International Journal of Advance Research and Innovative Ideas In Education, 12(2), pp. 1739-1744.
Chicago Style
Mulik, Rohan, et al. "Machine Learning Based Early Stage Diabetes Detection System." International Journal of Advance Research and Innovative Ideas In Education 12, no. 2 (2026): 1739-1744.
Turabian Style
Mulik, Rohan, et al. "Machine Learning Based Early Stage Diabetes Detection System." International Journal of Advance Research and Innovative Ideas In Education 12, no. 2 (2026): 1739-1744.
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