applied sciences  
An IoT Healthcare System Based on Fog Computing and Data Mining: A Diabetic Use Case  
[PERSON] 1, [PERSON] 2 and [PERSON] 2,*()  
1 Faculty of IT, Azad University of North Tehran Branch, Tehran 1876, Iran 1  
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
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Footnote 6:  
A local analysis enhances the reliability of diabetic patient monitoring systems by minimizing reliance on external networks and enhancing data processing efficiency. * Fog computing and local data mining technologies accelerate information transmission and processing, resulting in decreased response times for detecting blood sugar abnormalities. * Data mining techniques enable the detection of abnormalities tailored to each patient's unique biological profile, thereby improving the effectiveness of alert mechanisms. In alignment with the above assumptions, our primary research question is as follows: _\"How can we optimize diabetic patient monitoring systems by leveraging local analysis to enhance reliability, utilizing fog computing and local data mining to expedite information transmission and processing for reduced response times, and employing data mining to detect abnormalities tailored to the patient's biological profile?\"_  
In this study, a novel IoT healthcare system leveraging fog computing and data mining techniques is proposed, focusing on diabetes management. The research addresses two major challenges in healthcare: the timely detection of abnormalities and accurate diagnosis tailored to individual patient conditions. By utilizing fog computing, which processes data at the network edge, and the KNN algorithm for data analysis, the system enhances response times and reliability. Through simulations and real-world testing with diabetic patient data, the study demonstrates the system's effectiveness in providing prompt alerts and accurate notifications. This approach highlights the potential for improving patient care by integrating advanced technologies to optimize monitoring and intervention processes. The motivation behind this study stems from the urgent need to enhance diabetes management and patient care through advanced technology. Diabetes is a widespread chronic condition requiring continuous monitoring and timely intervention to prevent severe complications. Traditional healthcare systems often struggle with delayed responses and generalized treatment approaches that may not cater to individual patient needs. By integrating fog computing and data mining techniques, this study aims to address these challenges by enabling real-time data processing and personalized analysis. The goal is to improve the accuracy of diagnoses, speed up response times, and ultimately provide more effective and timely care for diabetic patients, potentially reducing healthcare costs and improving quality of life. With the described motivation, the main contributions of this paper are listed as follows:  
* **Integration of IoT and Fog Computing:** This study presents a novel integration of IoT and fog computing technologies within healthcare systems, specifically targeting the management of chronic conditions like diabetes.