Advanced Artificial Intelligence and Machine Learning Driven Data Analyses in Diabetes Mellitus Research

Aug 2, 2026

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Diabetes mellitus is addressed as a globally significant endocrine disorder through a PhD research project that applies machine learning to investigate diabetes-related data with precision and depth. The work is structured into three domains: predictive analysis, glucose quantification, and risk assessment for patients with pre-existing diabetes, including COVID-19 cases. Advanced deep learning architectures are developed for blood glucose forecasting, new methods improve glucose quantification, and thorough risk evaluations are performed. State-of-the-art algorithms support these pipelines, resulting in publication-ready research contributions aimed at advancing disease understanding, patient care, and healthcare resource allocation.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages183

Reading Time7 h 41 min

AccessFree to download

Acknowledgements

Abstract

Commentary

Research focus areas

Advanced Artificial Intelligence and Machine Learning Driven Data Analyses in Diabetes Mellitus Research By: Heydar Khadem Electronic and Electrical Engineering Department University of Sheffield PhD Dissertation 2024 Epigraph "Data is the oil of the 21st century, and analytics is the combustion engine." Peter Sondergaard Acknowledgements First of all, I would like to express my deepest gratitude to my principal supervisor, Professor Mohammed Benaissa, and my second supervisor, Dr Jackie Elliott, for their unwavering academic and pastoral support during my PhD. Their guidance has been invaluable. In addition, I would like to extend my sincere appreciation to my esteemed colleagues and coauthors, including Dr. Hoda Nemat, Dr. Mohammad R. Eissa, and Dr. Osamah Alrezj. The intellectual contributions and camaraderie we've shared throughout this journey have been of immeasurable value. Their insights have helped to sharpen my research and enrich my understanding. I also wish to acknowledge the often unseen yet essential work of the administrative and technical staff. Their tireless efforts have facilitated a seamless progression of my studies, and for this, I am deeply grateful. Lastly, my gratitude extends to the entire University of Sheffield community. The stimulating academic environment, coupled with a plethora of comprehensive resources, has significantly enhanced my research journey. This institution has provided me with more than just an education, it has been a pivotal part of my growth both academically and personally. My time at the University of Sheffield has been a profoundly enriching experience. Abstract Diabetes mellitus is an endocrine disorder of global significance. This PhD research project harnesses the capabilities of machine learning techniques for the meticulous investigation of data associated with diabetes mellitus. The formidable prevalence of the disease across the world, alongside the consequential burdens it imposes on healthcare systems, underscores the paramount importance of this research. The research is characteristically subdivided into three focal domains: predictive analysis, glucose quantification, and risk assessment relating to diabetes. Specifically, the research delves into advanced deep learning architectures for forecasting blood glucose levels, proposes methodologies for improving glucose quantification, and provides thorough risk assessments for COVID-19 patients with pre-existing diabetes mellitus. For each of these three domains, the research deploys state-of-the-art machine learning algorithms as a powerful apparatus to navigate the complexities of diabetes data, culminating in two research publications in reputable, peer-reviewed academic journals. Each publication illuminates the transformative potential of machine learning as a conduit for novel advancements within the respective domain. This in turn contributes to a more nuanced understanding of the disease, enhancement of patient care, and optimisation of healthcare resource allocation. Composed in a publication format, this dissertation is structured as a compilation of the six resultant articles, which are interconnected within the overarching framework of machine learning applications in diabetes research. As a whole, this extensive exploration of diabetes data through machine learning pipelines proffers novel insights and aims to make a substantial contribution to the academic field. Commentary In adherence to the University of Sheffield's code of practice, this dissertation adopts a publication format. According to the university’s guidelines, a dissertation in this format commences with a commentary, such as this one, which distils the central themes ofthe PhD research project and its corresponding publication outputs. Following this commentary, a compilation of research publications forms the primary body of the dissertation. The cornerstone of this PhD research project is the anal

FAQs

What are the three main research domains covered in the dissertation?

The dissertation focuses on predictive analysis, glucose quantification, and diabetes-related risk assessment. It includes work on blood glucose forecasting and risk evaluation for COVID-19 patients with pre-existing diabetes.

How does the research use machine learning in diabetes analysis?

Machine learning algorithms are used to navigate the complexities of diabetes data across the three domains. The work emphasizes advanced deep learning architectures for forecasting and data-driven methods for quantification and risk assessment.

What outputs and contributions does the dissertation emphasize?

The research is presented in a publication format and comprises a compilation of resultant articles. It reports state-of-the-art approaches that aim to improve understanding of diabetes, enhance patient care, and optimize healthcare resource allocation.

Aug 2, 2026

Upload by Levi