A Cloud-Enabled IoT Framework for Liver Disease Detection Using ML and Embedded Electronics

Abstract

The increasing prevalence of liver diseases worldwide necessitates innovative and efficient diagnostic approaches. This study presents a cloud-enabled Internet of Things (IoT) framework integrating machine learning (ML) algorithms and embedded electronics for real-time liver disease detection. The framework combines wearable sensors and embedded devices to collect vital physiological data, including liver enzyme levels, bilirubin concentration, and patient demographics. These data are transmitted to a cloud-based server through IoT communication protocols, where advanced ML models analyze the information to predict liver disease with high accuracy. The system employs lightweight algorithms to ensure low-latency processing and scalability for remote deployment in resource-constrained settings. Rigorous validation using clinical datasets demonstrates the framework's efficacy, achieving substantial precision and recall metrics. Furthermore, the integration of cloud computing enhances data accessibility, storage, and computational capabilities, while IoT components ensure continuous monitoring and seamless patient-doctor communication. This study highlights the potential of ML-powered IoT systems in improving early detection and personalized healthcare solutions for liver diseases, offering a transformative shift toward proactive and patient-centered medical care. The proposed framework addresses challenges such as real-time data synchronization, energy efficiency, and robust data security, paving the way for scalable, cost-effective, and reliable liver disease detection solutions.