Predicting Stroke Risk Based on ICD Codes Using Graph-Based Convolutional Neural Networks

Attila Tiba (), Tamás Bérczes, Attila Bérczes and Judit Zsuga
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Attila Tiba: Department of Data Science and Visualization, Faculty of Informatics, University of Debrecen, 4028 Debrecen, Hungary
Tamás Bérczes: Department of IT Systems and Networks, Faculty of Informatics, University of Debrecen, 4028 Debrecen, Hungary
Attila Bérczes: Institute of Mathematics, Faculty of Science and Technology, University of Debrecen, 4032 Debrecen, Hungary
Judit Zsuga: Department of Psychiatry, Faculty of Medicine, University of Debrecen, 4032 Debrecen, Hungary

Mathematics, 2024, vol. 12, issue 12, 1-15

Abstract: In recent years, convolutional neural networks (CNNs) have emerged as highly efficient architectures for image and audio classification tasks, gaining widespread adoption in state-of-the-art methodologies. While CNNs excel in machine learning scenarios where the data representation exhibits a grid structure, they face challenges in generalizing to other data types. For instance, they struggle with data structured on 3D meshes (e.g., measurements from a network of meteorological stations) or data represented by graph structures (e.g., molecular graphs). To address such challenges, the scientific literature proposes novel graph-based convolutional network architectures, extending the classical convolution concept to data structures defined by graphs. In this paper, we use such a deep learning architecture to examine graphs defined using the ICD-10 codes appearing in the medical data of patients who suffered hemorrhagic stroke in Hungary in the period 2006–2012. The purpose of the analysis is to predict the risk of stroke by examining a patient’s ICD graph. Finally, we also compare the effectiveness of this method with classical machine learning classification methods. The results demonstrate that the graph-based method can predict the risk of stroke with an accuracy of over 73%, which is more than 10% higher than the classical methods.

Keywords: ICD-10 codes; graph convolutional neural network; stroke prediction (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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