Risk Identification and Mitigation for Diabetes Prediction and Control Systems in Healthcare Software

International Journal of Computer Science and Engineering

© 2025 by SSRG - IJCSE Journal

Volume 12 Issue 5

Year of Publication : 2025

Authors : Sana Rizwan, Hassan Jamal, Asadullah, Rehan Rabbani Baig

10.14445/23488387/IJCSE-V12I5P103

How to Cite?

Sana Rizwan, Hassan Jamal, Asadullah, Rehan Rabbani Baig, "Risk Identification and Mitigation for Diabetes Prediction and Control Systems in Healthcare Software," SSRG International Journal of Computer Science and Engineering , vol. 12,  no. 5, pp. 19-28, 2025. Crossref, https://doi.org/10.14445/23488387/IJCSE-V12I5P103

Abstract:

Diabetes, a persistent metabolic disorder impacting over 400 million individuals globally, poses significant health risks and mortality, making it one of the foremost health challenges worldwide. Fueled by inactive lifestyles, poor dietary choices, and genetic factors, the prevalence of this condition is on the rise, potentially leading to serious complications such as cardiovascular disease, renal failure, and loss of vision. Early detection and proactive management are critical to reduce the societal and economic impact associated with diabetes and care to address these problems successfully.
This study introduces a revolutionary "Diabetes Prediction and Control System," a technology-driven tool designed to forecast diabetes risk and provide personalized management plans precisely. The system produces remarkable predictive results using sophisticated machine learning methods such as Random Forest and Logistic Regression. Its careful design includes robust risk management measures, including data encryption and privacy safeguards, to address typical data security problems.
Real-world testing validated the system's efficiency and user-friendliness, highlighting its capacity to integrate into healthcare policies smoothly and enable healthcare providers and patients. This platform stresses user-friendly elements, practical advice, and ethical concerns in addition to conventional tools. By combining modern technology with sensible medical strategies, this study helps to improve diabetes treatment by preparing scalable and efficient solutions to solve the diabetes epidemic.

Keywords:

Random Forest, Logistic Regression, IDF, SVM, Z-score, IQR, BMI, Risk Mitigation and contingency.

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