Fog Computing-Integrated ML-Based Framework and Solutions for Intelligent Systems: Digital Healthcare Applications

Introduction

Fog computing is a digital technology that extends cloud computing to tackle challenges in healthcare and other sectors. It involves decentralized data processing and analysis at the network's edge, closer to data sources, rather than distant cloud servers. This solution aligns with the sector's requirements for timely and secure handling of vast amounts of data. Fog computing significantly reduces latency in healthcare, enabling immediate decision-making by professionals and faster responses to patient needs. This is particularly important during emergency situations or real-time monitoring, as delays can be life-threatening. Fog computing is a technology that integrates with IoT devices in healthcare, enabling data processing and analysis closer to the source. This allows healthcare providers to use IoT for monitoring patients, managing equipment, and conducting medical research (Bakhshi & Balador, 2019; Kadu & Singh, 2023). Fog computing also enhances data security and privacy, minimizing the risk of data breaches during data transmission. This ensures the confidentiality and security of patients' personal and medical information.

Fog computing systems are highly adaptable, allowing for the expansion and contraction of computing resources as needed to handle varying workloads in healthcare. This flexibility is crucial for handling data volumes that can fluctuate greatly, from routine monitoring to sudden surges during public health crises. Fog computing offers reduced latency, enhanced IoT support, improved data security and privacy, and adaptable scalability, making it an essential technology for enhancing the efficiency and effectiveness of healthcare services and applications (Yang, Luo, Chu, Zhou, et al., 2020; Yazdani et al., 2023).

Machine learning (ML) techniques are rapidly advancing in healthcare, transforming various domains like diagnostics and personalized treatment. ML algorithms can analyze vast datasets like medical images, pathology slides, and patient records, aiding in early disease detection. For example, in radiology, ML models can detect anomalies in X-rays, MRIs, and CT scans, facilitating quicker and more accurate diagnoses. ML-powered diagnostic systems can also assist in early disease identification, improving patient outcomes like cancer. ML plays a crucial role in predicting patient outcomes and personalizing treatment plans (Maheswari et al., 2023; Ramudu et al., 2023). It analyzes patient data, including genomic information, history, and lifestyle, to develop predictive models. These models help clinicians anticipate disease progression and tailor treatment strategies. Personalized medicine relies on ML to identify effective treatment options, optimize therapies, and minimize adverse effects. ML also supports drug discovery and development by predicting potential candidates and identifying new applications, accelerating the process, reducing costs, and increasing the likelihood of successful outcomes (Maguluri et al., 2023; Syamala et al., 2023).

Machine learning (ML) is increasingly being used in healthcare for remote monitoring and wearable health devices. It allows algorithms to collect and interpret patient data, providing real-time feedback and timely interventions. This empowers patients to actively engage in their healthcare and provides valuable insights for healthcare professionals. ML has the potential to revolutionize patient care and streamline operations, improving healthcare outcomes and patient experiences (Maheswari et al., 2023; Veeranjaneyulu et al., 2023).