1
Kafkas University, Faculty of Economics and Administrative Sciences, Kars, Turkiye
Abstract
Heart diseases remain one of the leading causes of death worldwide, highlighting the urgent need for fast, reliable, and effective diagnostic systems that enable early detection and intervention. This necessity has become more prominent with the increasing integration of digital transformation into the healthcare sector. In this context, advanced technologies such as Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have become integral components of clinical decision support systems. This study presents the development of a DL model based on Artificial Neural Networks (ANN) for the early diagnosis of heart disease. The dataset, comprising clinical and demographic characteristics of 303 individuals, was obtained from the open-source UCI platform. The data were preprocessed and divided into training and testing sets, and early stopping was applied during training to prevent overfitting. The developed model achieved an accuracy rate of 90.16%, demonstrating superior performance compared to traditional models. These findings suggest that AI and DL-based systems hold significant potential as reliable and effective decision support mechanisms in the healthcare field, particularly in the diagnosis of heart diseases, within the broader framework of digital transformation.
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