HealthFaaS: AI-Based Smart Healthcare System for Heart Patients Using Serverless Computing


AI-based Multi Disease Detection using Machine Learning



Heart disease is one of the leading causes of death worldwide, and with early detection, mortality rates can be reduced. Well-known studies have shown that the latest artificial intelligence (AI) can be used to determine the risk of heart disease. However, existing studies did not consider dynamic scalability to get the best performance from these AI models in case of an increasing number of users. To solve this problem, we proposed an AI-powered smart healthcare framework called HealthFaaS, using the Internet of Things (IoT) and a Serverless Computing environment to reduce heart disease-related deaths and prevent financial losses by reducing misdiagnoses. HealthFaaS framework collects health data from users via IoT devices and sends it to AI models deployed on a Google Cloud Platform (GCP)-based serverless computing environment due to its advantages, such as dynamic scalability, less operational complexity, and a pay-as-you-go pricing model. The performance of five different AI models for heart disease risk detection is evaluated and compared based on key parameters, such as accuracy, precision, recall, F-Score, and AUC. Experimental results demonstrate that the light gradient boosting machine model gives the highest success in detecting heart diseases with an accuracy rate of 91.80%. Further, we have tested the performance of the HealthFaaS framework in terms of Quality-of-Service (QoS) parameters, such as throughput and latency against the increasing number of users and compared it with a non-serverless platform. In addition, we have also evaluated the cold start latency using a serverless platform which determined that the amount of memory and the software language makes a direct impact on the cold start latency.



The study employs advanced machine learning algorithms to analyze the complex interactions between various factors contributing to the onset of multiple diseases. By leveraging the power of artificial intelligence, our models aim to identify patterns and correlations across different diseases, enabling a holistic and early detection approach. The research encompasses a range of prevalent diseases, including but not limited to heart disease, liver disease, breast cancer, and diabetes. Through the development and validation of integrated machine learning models, we aim to provide a versatile and accurate diagnostic tool for healthcare professionals.

This approach holds the potential to revolutionize disease detection by offering a comprehensive understanding of an individual’s health status, facilitating timely interventions and personalized treatment plans. Our findings highlight the feasibility and effectiveness of using machine learning to address the complexity of multi-disease detection.

The implications of this research extend beyond individual disease silos, emphasizing a holistic perspective that aligns with the evolving landscape of precision medicine. As healthcare moves towards a more proactive and personalized paradigm, the integration of machine learning in multi-disease detection stands as a promising avenue for improving patient outcomes and optimizing healthcare resources.


Software Requirements:

  • Front End – Anaconda IDE
  • Backend – SQL
  • Language – Python 3.8

Hardware Requirements

  • •Hard Disk: Greater than 500 GB
  • •RAM: Greater than 4 GB
  • •Processor: I3 and Above


Including Packages


  • * Base Paper
  • * Complete Source Code
  • * Complete Documentation
  • * Complete Presentation Slides
  • * Flow Diagram
  • * Database File
  • * Screenshots
  • * Execution Procedure
  • * Readme File
  • * Addons
  • * Video Tutorials
  • * Supporting Softwares

Specialization =======================

  • * 24/7 Support * Ticketing System
  • * Voice Conference
  • * Video On Demand 
  • * Remote Connectivity
  • * Code Customization
  • * Document Customization 
  • * Live Chat Support

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