Machine Learning Algorithms for Early Prediction of Diabetes: A Mini-Review

Rouaa Alzoubi, Saad Harous

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Diabetes is a chronic disease caused by increased blood glucose levels. Several physical and chemical tests can be used to diagnose this disease. Untreated and undiagnosed diabetes, on the other hand, can harm human organs such as the eye, heart, kidneys, and nerves and may even lead to death. As a result, early detection and analysis of diabetes can help reduce the death rate. Machine learning and deep learning models have been used recently in many medical fields, and their efficiency for the early diagnosis of different diseases has been noticed. This study aims to discuss the different state-of-the-art algorithms that researchers have implemented for the early prediction of diabetes. The work focuses on highlighting different techniques used in the literature and the effectiveness of those techniques, which can help in knowing the current limitations of the work and making more improvements to it. As a result, our research showed that the random forest and KNN algorithms outperformed other algorithms in the literature with an accuracy of 98% in the early prediction of diabetes.

Original languageEnglish
Title of host publication2022 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages401-405
Number of pages5
ISBN (Electronic)9781665456005
DOIs
Publication statusPublished - 2022
Externally publishedYes
Event2022 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2022 - Ras Al Khaimah, United Arab Emirates
Duration: Nov 23 2022Nov 25 2022

Publication series

Name2022 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2022

Conference

Conference2022 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2022
Country/TerritoryUnited Arab Emirates
CityRas Al Khaimah
Period11/23/2211/25/22

Keywords

  • Diabetes
  • Diabetes mellitus
  • Disorder
  • Machine learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Signal Processing
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Safety, Risk, Reliability and Quality
  • Control and Optimization

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