Web-Based Software Development: Diabetes Mellitus Prediction

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Diabetes mellitus is a chronic disease that has become a serious global health problem. The high prevalence of diabetes mellitus and the lack of public awareness of the risk of diabetes are serious problems. Early detection and prevention of this disease are important. However, early detection is often not optimized. The development of information and communication technology causes the use of technologies such as machine learning and web-based applications to be a potential solution to increase public awareness of the risks and early detection of diabetes mellitus. Therefore, this research develops a web-based diabetes mellitus prediction application using machine learning technology with history and advice features that can be used as a health assistant for the general public regarding their diabetes risk.

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Engineering Headway (Volume 27)

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343-355

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October 2025

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© 2025 Trans Tech Publications Ltd. All Rights Reserved

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[1] WHO, "Diabetes" World Health Organization."

Google Scholar

[2] M. Bergman, M. Buysschaert, P. E. Schwarz, A. Albright, K. V. Narayan, and D. Yach, "Diabetes prevention: global health policy and perspectives from the ground," Diabetes Management, vol. 2, no. 4, p.309–321, Jul. 2012.

DOI: 10.2217/dmt.12.34

Google Scholar

[3] J. Li, L. Gu, and Y. Guo, "An educational intervention on foot self‐care behaviour among diabetic retinopathy patients with visual disability and their primary caregivers," J Clin Nurs, vol. 28, no. 13–14, p.2506–2516, Jul. 2019.

DOI: 10.1111/jocn.14810

Google Scholar

[4] M. Bergman, M. Buysschaert, P. E. Schwarz, A. Albright, K. V. Narayan, and D. Yach, "Diabetes prevention: global health policy and perspectives from the ground," Diabetes Management, vol. 2, no. 4, p.309–321, Jul. 2012.

DOI: 10.2217/dmt.12.34

Google Scholar

[5] Y. Wang, S. W. Buchholz, M. Murphy, and A. M. Moss, "A Diabetes Screening and Education Program for Chinese American Food Service Employees Delivered in Chinese," Workplace Health Saf, vol. 67, no. 5, p.209–217, May 2019.

DOI: 10.1177/2165079918823218

Google Scholar

[6] R. Konda, A. Ramineni, J. J, N. Singavajhala, and S. A. Vanka, "Integrated Embedded system for detecting diabetes mellitus using various machine learning techniques," EAI Endorsed Trans Pervasive Health Technol, vol. 10, Mar. 2024.

DOI: 10.4108/eetpht.10.5497

Google Scholar

[7] F. Batool, K. Malik, L. Meraj, S. Siddiq, A. Majeed, and S. Khan, "Awareness About Diabetes and Its Complications Among Patients With Diabetes Mellitus," Journal of Rawalpindi Medical College, vol. 27, no. 4, Dec. 2023.

DOI: 10.37939/jrmc.v27i4.2381

Google Scholar

[8] S. A. Hossain, R. Rafi, B. A. Saherawala, B. K. M. Goud, and J. B. Kumar, "Evaluation Of Clinical Awareness Of Diabetic Mellitus Among Rakmhsu Medical Students- A Questionnaire Survey," Int J Med Biomed Stud, vol. 5, no. 12, Jan. 2022.

DOI: 10.32553/ijmbs.v5i12.2306

Google Scholar

[9] M. A. and V.-I. M., "Existential Risk Prediction Models for Diabetes Mellitus," British Journal of Computer, Networking and Information Technology, vol. 5, no. 1, p.144–157, Nov. 2022.

DOI: 10.52589/BJCNIT-PM3CRE7I

Google Scholar

[10] O. Filipova and O. Nikiforova, "Definition of the Criteria for Layout of the UML Use Case Diagrams," Applied Computer Systems, vol. 24, no. 1, p.75–81, May 2019.

DOI: 10.2478/acss-2019-0010

Google Scholar

[11] A. Y. Aleryani, "Analyzing Data Flow: A Comparison between Data Flow Diagrams (DFD) and User Case Diagrams (UCD) in Information Systems Development," European Modern Studies Journal, vol. 8, no. 1, p.313–320, Mar. 2024.

DOI: 10.59573/emsj.8(1).2024.28

Google Scholar

[12] R. Fauzan, D. Siahaan, S. Rochimah, and E. Triandini, "Automated Class Diagram Assessment using Semantic and Structural Similarities," International Journal of Intelligent Engineering and Systems, vol. 14, no. 2, p.52–66, Apr. 2021.

DOI: 10.22266/ijies2021.0430.06

Google Scholar

[13] Q. Yao and X. Cui, "Approach to Check the Consistency between the UML2.0 Dynamic Diagrams," in 2015 Fifth International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC), IEEE, Sep. 2015, p.1115–1119.

DOI: 10.1109/IMCCC.2015.240

Google Scholar

[14] B. Hu, "Applications of Interaction Design on the Website Interface Design," Adv Mat Res, vol. 468–471, p.378–381, Feb. 2012.

DOI: 10.4028/www.scientific.net/AMR.468-471.378

Google Scholar

[15] V. Chandwani, V. Agrawal, and R. Nagar, "Modeling Slump of Ready Mix Concrete Using Genetically Evolved Artificial Neural Networks," Advances in Artificial Neural Systems, vol. 2014, p.1–9, Nov. 2014.

DOI: 10.1155/2014/629137

Google Scholar

[16] D. R. Hamad, "Logical Database Design," International Journal Of Computers & Technology, vol. 15, no. 12, p.7329–7332, Oct. 2016.

DOI: 10.24297/ijct.v15i12.4344

Google Scholar

[17] Md. S. Reza, U. Hafsha, R. Amin, R. Yasmin, and S. Ruhi, "Improving SVM performance for type II diabetes prediction with an improved non-linear kernel: Insights from the PIMA dataset," Computer Methods and Programs in Biomedicine Update, vol. 4, p.100118, 2023.

DOI: 10.1016/j.cmpbup.2023.100118

Google Scholar

[18] B. S. Ahamed, M. S. Arya, S. K. B. Sangeetha, and N. V. Auxilia Osvin, "Diabetes Mellitus Disease Prediction and Type Classification Involving Predictive Modeling Using Machine Learning Techniques and Classifiers," Applied Computational Intelligence and Soft Computing, vol. 2022, p.1–11, Dec. 2022.

DOI: 10.1155/2022/7899364

Google Scholar

[19] O. Shmatko, A. Goloskokova, O. Korol, and I. Rahimova, "Comparison Of Machine Learning Methods For A Diabetes Prediction Information System," Системи управління, навігації та зв'язку. Збірник наукових праць, vol. 4, no. 66, p.73–78, Dec. 2021.

DOI: 10.26906/SUNZ.2021.4.073

Google Scholar

[20] A. Mujumdar and V. Vaidehi, "Diabetes Prediction using Machine Learning Algorithms," Procedia Comput Sci, vol. 165, p.292–299, 2019.

DOI: 10.1016/j.procs.2020.01.047

Google Scholar

[21] A. Zale and N. Mathioudakis, "Machine Learning Models for Inpatient Glucose Prediction," Curr Diab Rep, vol. 22, no. 8, p.353–364, Aug. 2022.

DOI: 10.1007/s11892-022-01477-w

Google Scholar