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All Journal International Journal of Electrical and Computer Engineering
Ghaith M. Jaradat
Amman Arab University
Computer Science & IT Electrical & Electronics Engineering

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A comprehensive study of machine learning for predicting cardiovascular disease using Weka and SPSS tools Belal Abuhaija; Aladeen Alloubani; Mohammad Almatari; Ghaith M. Jaradat; Hemn Barzan Abdallah; Abdallah Mohd Abualkishik; Mutasem Khalil Alsmadi
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp1891-1902

Abstract

Artificial intelligence (AI) is simulating human intelligence processes by machines and software simulators to help humans in making accurate, informed, and fast decisions based on data analysis. The medical field can make use of such AI simulators because medical data records are enormous with many overlapping parameters. Using in-depth classification techniques and data analysis can be the first step in identifying and reducing the risk factors. In this research, we are evaluating a dataset of cardiovascular abnormalities affecting a group of potential patients. We aim to employ the help of AI simulators such as Weka to understand the effect of each parameter on the risk of suffering from cardiovascular disease (CVD). We are utilizing seven classes, such as baseline accuracy, naïve Bayes, k-nearest neighbor, decision tree, support vector machine, linear regression, and artificial neural network multilayer perceptron. The classifiers are assisted by a correlation-based filter to select the most influential attributes that may have an impact on obtaining a higher classification accuracy. Analysis of the results based on sensitivity, specificity, accuracy, and precision results from Weka and Statistical Package for Social Sciences (SPSS) is illustrated. A decision tree method (J48) demonstrated its ability to classify CVD cases with high accuracy 95.76%.
Susceptible exposed infectious recovered-machine learning for COVID-19 prediction in Saudi Arabia Mutasem K. Alsmadi; Ghaith M. Jaradat; Sami A. Abahussain; Mohammed Fahed Tayfour; Usama A. Badawi; Hayat Alfagham; Muneerah Ebrahem Alshabanah; Daniah Abdulrahman Alrajhi; Hanouf Naif ALkhaldi; Njoud Ahmad Altuwaijri; Hany Answer ShoShan; Hayah Mohamed Abouelnaga; Ahmed Baz Mohamed Metwally
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp4761-4776

Abstract

Susceptible exposed infectious recovered (SEIR) is among the epidemiological models used in forecasting the spread of disease in large populations. SEIR is a fitting model for coronavirus disease (COVID-19) spread prediction. Somehow, in its original form, SEIR could not measure the impact of lockdowns. So, in the SEIR equations system utilized in this study, a variable was included to evaluate the impact of varying levels of social distance on the transmission of COVID-19. Additionally, we applied artificial intelligence utilizing the deep neural network machine learning (ML) technique. On the initial spread data for Saudi Arabia that were available up to June 25th, 2021, this improved SEIR model was used. The study shows possible infection to around 3.1 million persons without lockdown in Saudi Arabia at the peak of spread, which lasts for about 3 months beginning from the lockdown date (March 21st). On the other hand, the Kingdom's current partial lockdown policy was estimated to cut the estimated number of infections to 0.5 million over nine months. The data shows that stricter lockdowns may successfully flatten the COVID-19 graph curve in Saudi Arabia. We successfully predicted the COVID-19 epidemic's peaks and sizes using our modified deep neural network (DNN) and SEIR model.
Co-Authors Abdallah Mohd Abualkishik Ahmed Baz Mohamed Metwally Aladeen Alloubani Belal Abuhaija Daniah Abdulrahman Alrajhi Hanouf Naif ALkhaldi Hany Answer ShoShan Hayah Mohamed Abouelnaga Hayat Alfagham Hemn Barzan Abdallah Mohammad Almatari Mohammed Fahed Tayfour Muneerah Ebrahem Alshabanah Mutasem K. Alsmadi Mutasem Khalil Alsmadi Njoud Ahmad Altuwaijri Sami A. Abahussain Usama A. Badawi