Authors Abdulrahman Ahmed Jasim, Layth Rafea Hazim, Hayder Mohammedqasim, Roa’a Mohammedqasem, Oguz Ata, Omar Hussein Salman Publication date 2024/7 Journal The Journal of Supercomputing Volume 80 Issue 11 Pages 15664-15689 Publisher Springer US Description One of the most fatal and serious diseases that humans have encountered is diabetes, an illness affecting thousands of individuals yearly. In this era of digital systems, diabetes prediction based on machine learning (ML) is gaining high momentum. One of the benefits of treating patients early in the course of their noncommunicable diseases (NCDs) is that they can avoid costly therapies when the illness worsens later in life. Incidentally, diabetes is complicated by the dearth of medical professionals in underserved areas, such as distant rural communities. In these situations, the Internet of Medical Things and machine learning (ML) models can be used to offer healthcare practitioners the necessary prediction tools to more effectively and timely make decisions, thus assisting the early identification and diagnosis of NCDs. In this study, four conventional and hyper-AdaBoost ML models were trained and tested on the … Total citations Cited by 25 20242025 Scholar articles e-Diagnostic system for diabetes disease prediction on an IoMT environment-based hyper AdaBoost machine learning model AA Jasim, LR Hazim, H Mohammedqasim… - The Journal of Supercomputing, 2024 Cited by 25 Related articles All 5 versions

e-Diagnostic system for diabetes disease prediction on an IoMT environment-based hyper AdaBoost machine learning model

Authors:
Abdulrahman Ahmed Jasim, Layth Rafea Hazim, Hayder Mohammedqasim, Roa’a Mohammedqasem, Oguz Ata, Omar Hussein Salman

Publication date: 2024/7

Journal: The Journal of Supercomputing

Volume: 80

Issue: 11

Pages: 15664-15689

Publisher: Springer US

Description:
One of the most fatal and serious diseases that humans have encountered is diabetes, an illness affecting thousands of individuals yearly. In this era of digital systems, diabetes prediction based on machine learning (ML) is gaining high momentum. One of the benefits of treating patients early in the course of their noncommunicable diseases (NCDs) is that they can avoid costly therapies when the illness worsens later in life. Incidentally, diabetes is complicated by the dearth of medical professionals in underserved areas, such as distant rural communities. In these situations, the Internet of Medical Things and machine learning (ML) models can be used to offer healthcare practitioners the necessary prediction tools to more effectively and timely make decisions, thus assisting the early identification and diagnosis of NCDs. In this study, four conventional and hyper-AdaBoost ML models were trained and tested on the …

Total citations: Cited by 25

Scholar articles:
e-Diagnostic system for diabetes disease prediction on an IoMT environment-based hyper AdaBoost machine learning model
AA Jasim, LR Hazim, H Mohammedqasim… – The Journal of Supercomputing, 2024