Early detection of cardiac disease can be achieved by the integration of AI, IoT, and Cloud computing

Heart disease and stroke are the world’s two leading causes of death, yet both can be difficult to detect. Early diagnosis of heart disease – which includes complications such as heart attacks and strokes – could save lives. To help with early detection, researchers at Queen Mary, University of London and other UK institutions are proposing the use of IoT devices, AI, and cloud computing to provide real-time alerts when someone is experiencing a suspected cardiovascular emergency.  

Fortunately, one of AI’s specialties involves identifying anomalies in data, detecting subtleties in situations that are less obvious to the human eye. Golec has proposed that IoT devices (including smartwatches and other wearable devices) can be used to monitor a person’s vital signs and then transmit that data to the cloud, where an AI algorithm analyses it. If the person experiences a cardiovascular complication, the system sends an automatic alert to their doctor and/or the closest health-service provider. The platform is called HealthFaaS—a reference to the popular cloud-computing term 'function as a service' (FaaS). Golec and his colleagues describe how they created and tested HealthFaaS in a study published 18 May in the IEEE Internet of Things Journal. The researchers analysed five different AI models trained to detect heart complications, based on such factors as accuracy, precision, recall, and ranked predictions. The results revealed that the models achieved an accuracy in heart-disease risk detection of between 83 and 92 percent. Next, the team evaluated how well the top-ranked model (called LightGMB) worked on a serverless platform (Google Cloud Functions) compared to a non-serverless platform (Heroku). They discovered that a serverless platform achieves greater throughput and lower latency than the non-serverless platform—especially as the number of users increased. For example, say that 500 people are using the system at the same time. If the number of users suddenly increases to 10,000, non-serverless platforms will crash and may not be able to respond. “But the serverless platform can respond without crashing by automatically increasing resources,” says Golec. “With HealthFaaS, we used a serverless platform because it can respond to a high number of users simultaneously, thanks to its dynamic scalability feature. It also offers benefits such as less operational complexity and pay-as-you-go pricing.” However, he notes, privacy and security can still be an issue. To address this, he proposes adding security methods such as Blockchain, OAuth 2.0, and Transport Layer Security to HealthFaas.  

In future work, Golec aims to create a new framework to ensure security and privacy in smart health systems. He is also considering ways to combine AI and serverless computing to make the computing efficiency even greater for time sensitive IoT applications, such as instant patient follow-up and autonomous vehicles. Golec and his colleague's study "HealthFaaS: AI-based Smart Healthcare System for Heart Patients using Serverless Computing" was entitled to be published in the IEEE IoT journal. This work, which received attention within academia and in the private sector, was featured in the international media; IEEE Spectrum, ACM Tech News, Edge Impulse.  

Detailed information and a presentation video for HealthFaaS can be accessed from YouTube: https://www.youtube.com/watch?v=aGES8TSoIcw  

Publication Details 

Muhammed Golec; Sukhpal Singh Gill; Ajith Kumar Parlikad; Steve Uhlig “HealthFaaS: AI based Smart Healthcare System for Heart Patients using Serverless Computing,” IEEE Internet of Things Journal, 2023. DOI: https://doi.org/10.1109/JIOT.2023.3277500  

Read the full publication here: https://ieeexplore.ieee.org/abstract/document/10129153