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This Author published in this journals
All Journal International Journal of Public Health Science (IJPHS) Indonesian Journal of Electrical Engineering and Computer Science
Shadi Banitaan
University of Detroit Mercy
Health Professions

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Using Data Mining to Predict Possible Future Depression Cases Kevin Daimi; Shadi Banitaan
International Journal of Public Health Science (IJPHS) Vol 3, No 4: December 2014
Publisher : Intelektual Pustaka Media Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (400.213 KB) | DOI: 10.11591/ijphs.v3i4.4697

Abstract

Depression is a disorder characterized by misery and gloominess felt over a period of time. Some symptoms of depression overlap with somatic illnesses implying considerable difficulty in diagnosing it. This paper contributes to its diagnosis through the application of data mining, namely classification, to predict patients who will most likely develop depression or are currently suffering from depression. Synthetic data is used for this study. To acquire the results, the popular suite of machine learning software, WEKA, is used.
Respiratory failure in COVID-19 patients a comparative study of smokers to nonsmokers Mohammad Kharabsheh; Shadi Banitaan; Hakam W. Alomari; Mohammad Alshirah; Sukaina Alzyoud
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 2: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i2.pp1127-1137

Abstract

For many decades, smoking tobacco has been a crucial concern due to respiratory failure. The potential relationship between smoking and COVID-19 has been recently investigated. In this paper, we study and investigate the role of the decision support system to predict the ratio of respiratory failure in smokers versus non-smokers among COVID-19 patients. We employed a classifier that predicts the ratio of respiratory failure as well as the ratio of the death toll between smokers and non-smokers using machine learning methods. The employed model demonstrate a prediction accuracy of 77% when applied on a sample from 23 countries that confirmed the highest number of COVID-19 patients. This was obtained from The World Bank Data-Health Nutrition and Population Statistics. As a result, a strong (significant) relationship between smoking tobacco and COVID-19 was illustrated by the employed model. Our approach achieves a good recall (78%). Thus, smokers are more susceptible to respiratory failure than non-smokers, as COVID-19 complications.
Co-Authors Hakam W. Alomari Kevin Daimi Mohammad Alshirah Mohammad Kharabsheh Sukaina Alzyoud