Convolutional neural network-based cardiovascular disease prediction with adaptability

Jianfeng Wu; Xiaonuo Liu; Boxiao Yang; Lianggui Liu

doi:10.1117/12.3034761

11 July 2024 Convolutional neural network-based cardiovascular disease prediction with adaptability

Jianfeng Wu, Xiaonuo Liu, Boxiao Yang, Lianggui Liu

Proceedings Volume 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024); 132102X (2024) https://doi.org/10.1117/12.3034761
Event: Third International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 2024, Wuhan, China

Abstract

Cardiovascular diseases (CVD) are a leading cause of global mortality, as documented by the World Health Organization (WHO), resulting in a significant annual death toll. Accurate forecasting of CVD has the potential to significantly improve patient outcomes and potentially save lives. The advent of the COVID-19 pandemic has necessitated the adaptation of traditional disease prediction methodologies, underscoring the importance of a unified and precise predictive system. The integration of Artificial Intelligence (AI) in disease diagnosis and identification has been transformative, largely due to the rapid progress and reliability of deep learning technologies. This study proposes the application of Convolutional Neural Networks (CNNs) to the heart disease dataset from the University of California, Irvine (UCI) repository, employing machine learning (ML) techniques for the early detection of CVD. The analysis encompasses 14 critical attributes from the dataset. The performance of the proposed model is assessed using accuracy and confusion matrix evaluation metrics, which are employed to validate multiple prospective findings. To refine the dataset, the Isolation Forest algorithm is utilized to eliminate irrelevant features, and data standardization techniques are implemented to enhance the model's predictive accuracy. The application of deep learning methodologies has resulted in an impressive accuracy rate of 98%.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jianfeng Wu, Xiaonuo Liu, Boxiao Yang, and Lianggui Liu "Convolutional neural network-based cardiovascular disease prediction with adaptability", Proc. SPIE 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024), 132102X (11 July 2024); https://doi.org/10.1117/12.3034761

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KEYWORDS

Cardiovascular disorders

Neurological disorders

Heart

Convolutional neural networks

Deep learning

Machine learning

Binary data

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