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All Journal International Journal of Electrical and Computer Engineering
Ali Alghamdi, Fahad
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Computer Science & IT Electrical & Electronics Engineering

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Cardiovascular disease risk factors prediction using deep learning convolutional neural networks Almatari, Mohammad; Abuhaija, Belal; Alloubani, Aladeen; Haddad, Firas; M. Jaradat, Ghaith; Qawqzeh, Yousef; Alsmadi, Mutasem Khalil; Ali Alghamdi, Fahad; Saad Alqurni, Jehad; Alodat, Lena; Dong, Linyinxue
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4471-4487

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 (???? = 0.004) and body mass index (???? = 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.
Cuckoo algorithm with great deluge local-search for feature selection problems Khalil Alsmadi, Mutasem; Alzaqebah, Malek; Jawarneh, Sana; Brini, Sami; Al-Marashdeh, Ibrahim; Briki, Khaoula; Alrefai, Nashat; Ali Alghamdi, Fahad; Al-Rashdan, Maen T.
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp4315-4326

Abstract

Feature selection problem is concerned with searching in a dataset for a set of features aiming to reduce the training time and enhance the accuracy of a classification method. Therefore, feature selection algorithms are proposed to choose important features from large and complex datasets. The cuckoo search (CS) algorithm is a type of natural-inspired optimization algorithms and is widely implemented to find the optimum solution for a specified problem. In this work, the cuckoo search algorithm is hybridized with a local search algorithm to find a satisfactory solution for the problem of feature selection. The great deluge (GD) algorithm is an iterative search procedure, that can accept some worse moves to find better solutions for the problem, also to increase the exploitation ability of CS. The comparison is also provided to examine the performance of the proposed method and the original CS algorithm. As result, using the UCI datasets the proposed algorithm outperforms the original algorithm and produces comparable results compared with some of the results from the literature.
Co-Authors Abuhaija, Belal Al-Marashdeh, Ibrahim Al-rashdan, Maen T. Alloubani, Aladeen Almatari, Mohammad Alodat, Lena Alrefai, Nashat Alsmadi, Mutasem Khalil Alzaqebah, Malek Briki, Khaoula Brini, Sami Dong, Linyinxue Haddad, Firas Jawarneh, Sana Khalil Alsmadi, Mutasem M. Jaradat, Ghaith Qawqzeh, Yousef Saad Alqurni, Jehad