[agents] IDMPF: intelligent diabetes mellitus prediction framework using machine learning

[Leila Fayez Ismail]

Dear Colleagues,

I will be very happy to hear from you about our published paper:

IDMPF: intelligent diabetes mellitus prediction framework using machine learning

https://www.emerald.com/insight/content/doi/10.1108/ACI-10-2020-0094/full/html<https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.emerald.com%2Finsight%2Fcontent%2Fdoi%2F10.1108%2FACI-10-2020-0094%2Ffull%2Fhtml&data=04%7C01%7CLeila%40uaeu.ac.ae%7C091813f356434f8099b908d9617c081f%7C97a92b044c8743419b08d8051ef8dce2%7C0%7C0%7C637648007601147090%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=nHUm9y7BujY5v6oNdLhBE0tUJxt%2BzaYXFZGUcOEfdt0%3D&reserved=0>

Please do not hesitate to contact me with your comments.

Abstract
Purpose
Machine Learning is an intelligent methodology used for prediction and has shown promising results in predictive classifications. One of the critical areas in which machine learning can save lives is diabetes prediction. Diabetes is a chronic disease and one of the 10 causes of death worldwide. It is expected that the total number of diabetes will be 700 million in 2045; a 51.18% increase compared to 2019. These are alarming figures, and therefore, it becomes an emergency to provide an accurate diabetes prediction.
Design/methodology/approach
Health professionals and stakeholders are striving for classification models to support prognosis of diabetes and formulate strategies for prevention. The authors conduct literature review of machine models and propose an intelligent framework for diabetes prediction.
Findings
The authors provide critical analysis of machine learning models, propose and evaluate an intelligent machine learning-based architecture for diabetes prediction. The authors implement and evaluate the decision tree (DT)-based random forest (RF) and support vector machine (SVM) learning models for diabetes prediction as the mostly used approaches in the literature using our framework.
Originality/value
This paper provides novel intelligent diabetes mellitus prediction framework (IDMPF) using machine learning. The framework is the result of a critical examination of prediction models in the literature and their application to diabetes. The authors identify the training methodologies, models evaluation strategies, the challenges in diabetes prediction and propose solutions within the framework. The research results can be used by health professionals, stakeholders, students and researchers working in the diabetes prediction area.


Kind regards,
Leila

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Leila Ismail, Ph.D.
Associate Professor, Dept. of Computer Science & Software Engineering
Founder and Director to Intelligent Distributed Computing & Systems (INDUCE) Research Laboratory
(Industry 4.0 projects and beyond)
[Beyond the sky's the limit]
College of IT, UAE University
P.O.Box 15551, Al-Ain, UAE
Telephone: +971-3-7135530


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