Authors : Nethra Sureshkumar; Ninad Laxmish Dixit; Rida Rabeea Fatima; Sahana P.; Shama; Shobha T.

Volume/Issue : Volume 10 - 2025, Issue 1 - January

Google Scholar : https://tinyurl.com/ychc5c3r

Scribd : https://tinyurl.com/2s3mk6s2

DOI : https://doi.org/10.5281/zenodo.14737876

Abstract : Recent developments in the Internet of Things, Artificial Intelligence, and cloud computing are transforming healthcare into a personalized and data-driven ecosystem. IoT-based healthcare systems, also known as the Internet of Medical Things, facilitate real- time monitoring through interconnected medical devices. However, the dynamic nature of healthcare environments demands adaptive systems capable of evolving over time. This research explores adaptive IoT systems inspired by Evolutionary Algorithms such as Genetic Algorithms, Ant Colony Optimiza- tion, and Particle Swarm Optimization. These bio-inspired models dynamically adjust device configurations and improve data processing, improving diagnostic accuracy and system performance. By using natural selection and evolutionary principles, the proposed framework integrates machine learning techniques, including Convolutional Neural Networks and Long Short-Term Memory networks, to analyse patient data and enhance decision-making accuracy. The application of these models addresses key challenges in scalability, energy efficiency especially for chronic disease management and elderly care, while also reducing costs and enhancing patient outcomes. This research is a mirror to the revolutionary changes that could be made by adaptive systems in the medical field. Keywords: Internet Of Things, Evolutionary Algorithms, Bio-inspired models, Data driven healthcare.

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