Predicting Long-Term Type 2 Diabetes with Artificial Intelligence (AI): A Scoping Review.

Journal: Studies in health technology and informatics
Published Date:

Abstract

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder that affects a significant portion of the global population. Artificial intelligence (AI) has emerged as a promising tool for predicting T2DM risk. To provide an overview of the AI techniques used for long-term prediction of T2DM and evaluate their performance, we conducted a scoping review using PRISMA-ScR. Of the 40 papers included in this review, 23 studies used Machine Learning (ML) as the most common AI technique, with Deep Learning (DL) models used exclusively in four studies. Of the 13 studies that used both ML and DL, 8 studies employed ensemble learning models, and SVM and RF were the most used individual classifiers. Our findings highlight the importance of accuracy and recall as validation metrics, with accuracy being used in 31 studies, followed by recall in 29 studies. These discoveries emphasize the critical role of high predictive accuracy and sensitivity in detecting positive T2DM cases.

Authors

  • Salleh Sonko
    College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
  • Fathima Lamya
    College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
  • Mahmood Alzubaidi
    College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
  • Hurmat Shah
    College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
  • Tanvir Alam
    College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
  • Zubair Shah
    College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
  • Mowafa Househ
    Faculty College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar1.