A Cognitive Medical Decision Support System for IoT-Based Human-Computer Interface in Pervasive Computing Environment

Abstract

In today’s advanced applications, such as memory interfaces, feature-based detection, and sensory games, human-computer interaction (HCI) plays a pivotal role. A medical decision support system (MDSS) emerges from the integration of a data system with resources for medical decision-making. Within MDSS, human-computer interaction and perceptual medical decision-making stand out as two highly valuable technologies. Systems enabled by the Internet of Things (IoT), which leverage decentralized, diverse communication and networking technology to cater to a wide range of end-users, are referred to as pervasive computing. A challenging aspect of pervasive computing is ensuring transparency in interaction, managing administration levels, and accommodating varying tolerance levels for widely dispersed users. This paper presents a uniquely flexible MDSS framework designed to enhance end-user confidence in the availability of MDSS through ubiquitous IoT devices within the context of HCI. This architecture utilizes recurring training to assess resource allocation based on demand and collaborative characteristics. Projected resource requirements enable pervasive computing to better serve end-users by reducing latency and increasing communication speeds for MDSS in HCI. The primary goal of this framework is to simplify the management of terminal transitions by facilitating the allocation and utilization of resources for data transfer from peripheral technology. Experimental analysis is employed to estimate the framework’s performance, utilizing various metrics to demonstrate its consistency. These metrics encompass responsiveness, transaction success rates, processed demands, application caseloads, capacity utilization, and memory usage. The uniquely flexible and distributed computing framework optimizes request handling, network accuracy, and memory utilization, resulting in reduced transaction failures and lower latency, ultimately leading to shorter response times. The proposed UFDSS maintains a transaction failure rate below 25% with increasing requests and achieves 100 MHz bandwidth utilization, surpassing other techniques capped at 80 MHz. UFDSS exhibits a lower average latency of around 30 ms for a range of energy data inputs. This uniquely flexible MDSS framework showcases its potential to enhance MDSS availability through IoT devices within HCI contexts. By optimizing resource allocation and utilization, it successfully reduces latency, improves communication speeds, and ultimately leads to shorter response times, contributing to more efficient and reliable medical decision support. Further, integrating generative AI into MDSS for IoT-based HCI could also enhance data-driven decision support.