An Efficient and Automated Smart Healthcare System Using Genetic Algorithm and Two-Level Filtering Scheme

This paper proposes an efficient and automated smart healthcare communication framework that integrates a two-level filtering scheme with a multi-objective Genetic Algorithm (GA) to enhance the reliability, timeliness, and energy efficiency of Internet of Medical Things (IoMT) systems. In the first stage, physiological signals collected from heterogeneous sensors (e.g., blood pressure, glucose level, ECG, patient movement and ambient temperature) are pre-processed using an adaptive least-mean-square (LMS) filter to suppress noise and motion artefacts, thereby improving signal quality prior to analysis. In the second stage, a GA-based optimization engine selects optimal routing paths and transmission parameters by jointly considering end-to-end delay, signal-to-noise ratio (SNR), energy consumption and packet loss ratio (PLR). The two-level filtering strategy ensures that only denoised and high-priority records are forwarded for further processing, enabling real-time clinical decision support and prioritization of critical patients. The proposed mechanism is evaluated via extensive simulations involving 30–100 devices and multiple generations, and is benchmarked against two existing smart healthcare schemes. The results demonstrate that the integrated GA and filtering approach significantly reduces end-to-end delay, communication latency and energy consumption, while improving packet delivery ratio, throughput, SNR and overall Quality of Service (QoS). These findings indicate that the proposed framework provides a scalable and intelligent communication backbone for early disease detection, continuous monitoring and timely intervention in smart healthcare environments.