Cardiovascular disease risk factors prediction using deep learning convolutional neural networks

Mohammad Almatari, Belal Abuhaija, Aladeen Alloubani, Firas Haddad, Ghaith M. Jaradat, Yousef Qawqzeh, Mutasem Khalil Alsmadi, Fahad Ali Alghamdi, Jehad Saad Alqurni, Lena Alodat, Linyinxue Dong

Abstract


Heart disease remains a leading cause of mortality worldwide, prompting healthcare researchers to leverage analytical tools for comprehensive data analysis. This study focuses on exploring crucial parameters and employing deep learning (DL) techniques to enhance understanding and prediction of cardiovascular disease (CVD) risk factors. Utilizing SPSS and Weka tools, a cross-sectional and correlational design was employed to analyze extensive medical datasets. Binomial regression analysis revealed significant associations between age (𝑝 = 0.004) and body mass index (𝑝 = 0.002) with CVD development, highlighting their importance as risk factors. Leveraging Weka's DL algorithms, a predictive model was constructed to classify CVD causes. Particularly, convolutional neural networks (CNN) showcased remarkable accuracy, reaching 98.64%. The findings underscore the elevated risk of CVD among university students and employees in Saudi Arabia, emphasizing the need for heightened awareness and preventive measures, including dietary improvements and increased physical activity. This study underscores the importance of further research to enhance CVD risk perception among students and individuals in similar settings.

Keywords


Attribute selection; Cardiovascular diseases; Classification; Convolutional neural networks; Deep learning; Weka

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DOI: http://doi.org/10.11591/ijece.v14i4.pp4471-4487

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International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578

This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).