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Prediction of Coronary Plaque Progression Using Data Mining and Artificial Neural Networks

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Disruptive Information Technologies for a Smart Society (ICIST 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 872))

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Abstract

Coronary artery disease represents one of the most significant health burdens worldwide. As the onset of the disease is multifactorial in nature, physicians are often struggling with determining the rate of progression of the arterial narrowing caused by buildup of plaque. Computational models have brought upon a significant shift in the paradigm and the advent of Big Data and machine learning has enabled far better understanding of disease dynamics. This study is based on a cohort of patients recruited through SMARTool project for whom an extensive monitoring system was set up. In order to select for the most influential parameters on the progression of coronary atherosclerosis, different feature selection algorithms were used. The dataset used for development of the system for prediction of coronary plaque progression consisted of demographic data, data considering comorbidities and different blood cholesterol parameters. The developed artificial neural network showed significant strength in diagnosing progression of coronary arterial plaque, and features selected within this study indicate the high potential of machine learning to be used in clinical practice as well as that specific types of cholesterol are important markers impacting plaque progression.

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Acknowledgements

This paper is supported by the DECODE project (www.decodeitn.eu) that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 956470. This article reflects only the author's view. The Commission is not responsible for any use that may be made of the information it contains.

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Authors and Affiliations

  1. Research and Development Center for Bioengineering, BioIRC, Prvoslava Stojanovica, 34000, Kragujevac, Serbia

    Lemana Spahić, Leo Benolić, Safi Ur Rehman Qamar, Vladimir Simic, Bogdan Miličević, Miljan Milošević, Tijana Geroski & Nenad Filipović

  2. Institute for Information Technologies, Jovana Cvijića, 34000, Kragujevac, Serbia

    Vladimir Simic & Miljan Milošević

  3. Faculty of Engineering, University of Kragujevac, Sestre Janjić, 34000, Kragujevac, Serbia

    Bogdan Miličević, Tijana Geroski & Nenad Filipović

  4. Metropolitan University, Tadeuša Košćuška 63, 11158, Belgrade, Serbia

    Miljan Milošević

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  1. Lemana Spahić
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  2. Leo Benolić
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  3. Safi Ur Rehman Qamar
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  4. Vladimir Simic
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  5. Bogdan Miličević
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  6. Miljan Milošević
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  7. Tijana Geroski
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  8. Nenad Filipović
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Correspondence to Lemana Spahić .

Editor information

Editors and Affiliations

  1. Faculty of Mechanical Engineering, University of Nis, Nis, Serbia

    Miroslav Trajanovic

  2. Faculty of Engineering, University of Kragujevac, Kragujevac, Serbia

    Nenad Filipovic

  3. Faculty of Mechanical Engineering, University of Nis, Nis, Serbia

    Milan Zdravkovic

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Spahić, L. et al. (2024). Prediction of Coronary Plaque Progression Using Data Mining and Artificial Neural Networks. In: Trajanovic, M., Filipovic, N., Zdravkovic, M. (eds) Disruptive Information Technologies for a Smart Society. ICIST 2023. Lecture Notes in Networks and Systems, vol 872. Springer, Cham. https://doi.org/10.1007/978-3-031-50755-7_1

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