Stochastic analysis of fog computing and machine learning for scalable low-latency healthcare monitoring.

In recent years, healthcare monitoring systems (HMS) have increasingly integrated the Internet of Medical Things (IoMT) with cloud computing, leading to challenges related to data latency and efficient processing. This paper addresses these issues by introducing a Machine Learning-based Medical Data Segmentation (ML-MDS) approach that employs a k-fold random forest technique for efficient health data classification and latency reduction in a fog-cloud environment. Our method significantly improves latency issues, enhancing the Quality of Service (QoS) in healthcare systems and demonstrating its adaptability in heterogeneous network scenarios. We specifically employ the Random Forest algorithm to mitigate the common problem of overfitting in machine learning models, ensuring broader applicability across various healthcare contexts. Additionally, by optimizing data processing in fog computing layers, we achieve a substantial reduction in overall latency between healthcare sensors and cloud servers. This improvement is evidenced through a comparative performance analysis with existing models. The proposed framework not only ensures secure and scalable management of IoMT health data but also incorporates a stochastic approach to mathematically formulate performance indicators for the HMS queuing model. This model effectively predicts system response times and assesses the computing resources required under varying workload conditions. Our simulation results show a classification accuracy of 92%, a 56% reduction in latency compared to existing models, and an overall enhancement in e-healthcare service quality. [ABSTRACT FROM AUTHOR]