Comparative Study of ML-Based Diabetes Detection Using IoT and Lab Data in Fog

Diabetes, as a chronic condition affecting millions of people worldwide, requires early diagnosis and continuous monitoring to prevent complications. The rise of machine learning (ML) applications in healthcare offers promising approaches for diagnosing and managing diabetes more effectively. Machine learning models can analyse extensive amounts of data to identify patterns that may be invisible to human clinicians, improving diagnosis accuracy and enabling personalized care. This study investigates the performance of four machine learning models—Decision Tree, Logistic Regression, Random Forest, and Support Vector Machine (SVM)—in detecting diabetes using two types of data: traditional lab-based data and real-time accessed data from Internet of Things (IoT) sensors. Data was collected from continuous glucose monitors (CGMs) and wearables, as well as clinical lab records in Albania. The results revealed that machine learning models applied to IoT data significantly outperformed those applied to lab data, demonstrating higher accuracy and better predictive metrics. The continuous monitoring enabled by IoT devices allows for real-time detection of glucose fluctuations, providing earlier and more precise diabetes diagnosis. Additionally, integrating IoT with fog computing reduces latency and enhances on-time decision-making, allowing for prompt interventions in patient care. The study highlights the transformative potential of combining IoT, machine learning, and fog computing to revolutionize healthcare, particularly the management of chronic diseases such as diabetes. The findings suggest that IoT-based systems should be adopted to improve diabetes detection and monitoring, allowing for a shift toward proactive healthcare solutions. Future research could explore the application of these technologies for managing other chronic conditions and optimizing machine-learning models for large-scale datasets.