An Intelligent Diagnostic System for Type 2 Diabetes Mellitus

A number of industries, including e-Healthcare, have recently seen an increase in interest in the Internet of Things (IoT). The IoT can be used to provide a range of services, including data storage, resource, and power management, as well as producing and computing services. Recently, the fog computing (FC), an extension of cloud computing (CC), has proven to be helpful in actual-world IoT applications and has fully fulfilled the shortcomings of the CC concepts. For the detection of Type 2 Diabetes Mellitus (T2DM), this study offers an intelligent decision support system framework based on proper analysis and IoT that can be used to improve diagnosis accuracy when using the Ensemble Machine Learning (EML) technique with mysterious data. From numerous trials, it has been found that adding fog worker nodes boosts training accuracy proportionately; the recorded accuracy of the master node with 5 fog worker nodes is 90.1%. Additionally, it is noted that the recorded latencies with the application of FC ideas are 48.9 ms, 57.6 ms, 66.4 ms, 74.3 ms, and 79.1 ms, respectively, for master node with 1, 2, 3, 4, and 5 worker nodes. These latencies are quite low compared to the reordered 1238.6 ms for CC concepts.