IDMPF - intelligent diabetes mellitus prediction framework using machine learning - research paper

Aug 2, 2026

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This research focuses on machine learning as an effective prediction methodology for diabetes, a chronic disease and major global cause of death. With projected diabetes growth by 2045, the work addresses the urgency of accurate prediction. Authors review machine learning models used for prognosis, then propose an intelligent machine learning framework (IDMPF). The framework supports health stakeholders and guides data analytics from understanding to deployment, evaluated using decision tree-based random forest and SVM.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages12

Reading Time30 min

AccessFree to download

Abstract

Introduction

Machine Learning Modeling and Classification

Proposed IDMPF Framework

Model Evaluation and Results

The current issue and full text archive of this journal is available on Emerald Insight at: [https://www.emerald.com/insight/2210-8327.htm](https://www.emerald.com/insight/2210-8327.htm) ACI 21,1/2 78 Received 19 October 2020 Revised 3 March 2021 14 April 2021 Accepted 7 May 2021 Applied Computing and Informatics Vol. 21 No. 1/2, 2025 pp. 78-89 Emerald Publishing Limited e-ISSN: 2210-8327 p-ISSN: 2634-1964 DOI 10. 1108/ACI-10-2020-0094 IDMPF: intelligent diabetes mellitus prediction framework using machine learning Leila Ismail and Huned Materwala Intelligent Distributed Computing and Systems Research Laboratory, Department of Computer Science and Software Engineering, United Arab Emirates University, AlAin, United Arab Emirates Abstract Purpose – Machine Learning is an intelligent methodology used for prediction and has shown promising results in predictive classifications. Oneof the critical areas in which machine learning can save lives is diabetes prediction. Diabetes is a chronic disease and oneof the10causes of death worldwide. It is expected that the total number of diabetes will be700million in2045;a51.18%increase compared to2019. 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. Keywords Artificial intelligence, Machine learning, Intelligent agents, Prediction, Data analytics, Health informatics, eHealth, Diabetes mellitus type 2 Paper type Research paper 1. Introduction Machine learning modeling is an intelligent way to extract the hidden relationship among different variables ina dataset. It has been used asa decision-support system for prediction indifferent applications’ domains such as healthcare, education and industry [1–3] . Machine learning models can be classified into three main categories: (1) supervised learning, (2) unsupervised learning and (3) semi-supervised learning [4](Figure S1 available at [https://](https://)[ ](https://)[github.com/Dr-Leila-Ismail](github.com/Dr-Leila-Ismail)) . The objective of a machine learning classification model is to predict the class of a given input data [5] . They are heavily used in healthcare for disease diagnosis and prognosis, fraud detection, drug efficiency and the development of a © Leila Ismail and Huned Materwala. Published in Applied Computing and Informatics. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may

FAQs

What problem does the IDMPF framework target?

The IDMPF framework targets accurate diabetes prediction, classifying individuals as diabetic or non-diabetic using machine learning.

How is the proposed diabetes prediction framework designed?

The authors propose an intelligent framework derived from critical examination of prediction models and their application to diabetes, following data analytics lifecycle principles.

Which machine learning models are evaluated in the study?

The study implements and evaluates decision tree (DT)-based random forest (RF) and support vector machine (SVM) learning models for diabetes prediction.

Aug 2, 2026

Upload by Asher