Utilizing Machine Learning to Detect Diabetes Risk

Abdullayev, Tural (2024) Utilizing Machine Learning to Detect Diabetes Risk. Masters thesis, Dublin, National College of Ireland.

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Abstract

Being in the 21st century, some of the problems facing individuals today should be bygones, and one of them is diabetes. With the current enhanced technologies, individuals should not be spending more money on treating and managing diabetes. This study will assess how Wearable Devices such as watches can be used to manage diabetes. Studies in the past have assessed the impact of diabetes globally. Scaling this down, the impact is based on different factors. This explains why some individuals are more prone to diabetes than others. Some factors include age, gender, type of food consumed, access to healthcare facilities, access to information, and also technological advancement. This study will focus on how technologies such as A.I. and ML can be used in the management of diabetes. This study will use the Pima Indians Dataset (Kaggle, n.d.) as a case study to assess diabetes and its impact on individuals. The Pima dataset has an accuracy of 76%. The methodology used in this study is Knowledge Discovery Databases (KDD). This is a comprehensive methodology that ensures accurate results. Its objective was to conduct intensive research on the implications of wearable devices such as smartwatches in the management of diabetes.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Jameel Syed, Muslim UNSPECIFIED
Uncontrolled Keywords:	Diabetes; ML; A.I.; Smart Watch; Wearable Devices
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QH Natural history > QH301 Biology > Methods of research. Technique. Experimental biology > Data processing. Bioinformatics > Artificial intelligence Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Artificial intelligence R Medicine > Healthcare Industry Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Artificial Intelligence for Business
Depositing User:	Ciara O'Brien
Date Deposited:	20 Jun 2025 11:28
Last Modified:	20 Jun 2025 11:28
URI:	https://norma.ncirl.ie/id/eprint/7975

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