HealthEdge: A Machine Learning-Based Smart Healthcare Framework for Prediction of Type 2 Diabetes in an Integrated IoT, Edge, and Cloud Computing System

Alain Hennebelle, Huned Materwala, Leila Ismail

Research output: Contribution to journalConference articlepeer-review

13 Citations (Scopus)

Abstract

Diabetes Mellitus has no permanent cure to date and is one of the leading causes of death globally. The alarming increase in diabetes calls for the need to take precautionary measures to avoid/predict the occurrence of diabetes. This paper proposes HealthEdge, a machine learning-based smart healthcare framework for type 2 diabetes prediction in an integrated IoT-edge-cloud computing system. Numerical experiments and comparative analysis were carried out between the two most used machine learning algorithms in the literature, Random Forest (RF) and Logistic Regression (LR), using two real-life diabetes datasets. The results show that RF predicts diabetes with 6% more accuracy on average compared to LR.

Original languageEnglish
Pages (from-to)331-338
Number of pages8
JournalProcedia Computer Science
Volume220
DOIs
Publication statusPublished - 2023
Event14th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023 - Leuven, Belgium
Duration: Mar 15 2023Mar 17 2023

Keywords

  • Artificial Intelligence (AI)
  • Cloud Computing
  • Diagnosis
  • Digital Health
  • Edge Computing
  • Internet of Things (IoT)
  • Logisitic Regression
  • Machine Learning
  • Prognosis
  • Random Forest
  • Risk Factors
  • Smart Connected Healthcare
  • Type 2 Diabetes Mellitus
  • eHealth

ASJC Scopus subject areas

  • General Computer Science

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