Discovering COVID-19 Death Patterns from Deceased Patients: A Case Study in Saudi Arabia

COVID-19 is a serious infection that cause severe injuries and deaths worldwide. The COVID-19 virus can infect people of all ages, especially the elderly. Furthermore, elderly who have co-morbid conditions (e.g., chronic conditions) are at an increased risk of death. At the present time, no approach exists that can facilitate the characterization of patterns of COVID-19 death. In this study, an approach to identify patterns of COVID-19 death efficiently and systematically is applied by adapting the Apriori algorithm. Validation and evaluation of the proposed approach are based on a robust and reliable dataset collected from Health Affairs in the Makkah region of Saudi Arabia. The study results show that there are strong associations between hypertension, diabetes, cardiovascular disease, and kidney disease and death among COVID-19 deceased patients © 2023, International Journal of Advanced Computer Science and Applications.All Rights Reserved.

[1]  Tarik K. Alafif,et al.  DISCOVID: discovering patterns of COVID-19 infection from recovered patients: a case study in Saudi Arabia , 2022, International Journal of Information Technology.

[2]  Yalan Chen,et al.  Chronic Diseases as a Predictor for Severity and Mortality of COVID-19: A Systematic Review With Cumulative Meta-Analysis , 2021, Frontiers in Medicine.

[3]  A. Robert,et al.  COVID-19 among people with diabetes mellitus in Saudi Arabia: Current situation and new perspectives , 2021, Diabetes & Metabolic Syndrome: Clinical Research & Reviews.

[4]  Awad Al-omari,et al.  Examining and investigating the impact of demographic characteristics and chronic diseases on mortality of COVID-19: Retrospective study , 2021, PloS one.

[5]  A. Hakawi,et al.  Epidemiological and clinical features of COVID-19 patients in Saudi Arabia , 2021, Journal of Infection and Public Health.

[6]  A. Barnawi,et al.  Machine and Deep Learning Towards COVID-19 Diagnosis and Treatment: Survey, Challenges, and Future Directions , 2020 .

[7]  Tarik K. Alafif,et al.  On the prediction of isolation, release, and decease states for COVID-19 patients: A case study in South Korea , 2020, ISA Transactions.

[8]  N. Saquib,et al.  The SARS-CoV-2 pandemic course in Saudi Arabia: A dynamic epidemiological model , 2020, Infectious Disease Modelling.

[9]  S. Yezli,et al.  COVID-19 social distancing in the Kingdom of Saudi Arabia: Bold measures in the face of political, economic, social and religious challenges , 2020, Travel Medicine and Infectious Disease.

[10]  Xuexian Fang,et al.  Comorbid Chronic Diseases and Acute Organ Injuries Are Strongly Correlated with Disease Severity and Mortality among COVID-19 Patients: A Systemic Review and Meta-Analysis , 2020, Research.

[11]  M. Hassounah,et al.  Digital Response During the COVID-19 Pandemic in Saudi Arabia , 2020, Journal of medical Internet research.

[12]  Z. Memish,et al.  COVID-19 in the Eastern Mediterranean Region and Saudi Arabia: prevention and therapeutic strategies , 2020, International Journal of Antimicrobial Agents.

[13]  Z. Memish,et al.  Saudi Arabia`s measures to curb the COVID-19 outbreak: temporary suspension of the Umrah pilgrimage , 2020, Journal of travel medicine.

[14]  Z. Memish,et al.  COVID-19 in the Shadows of MERS-CoV in the Kingdom of Saudi Arabia , 2020, Journal of epidemiology and global health.

[15]  Lihua Li,et al.  Association Analysis of Serial Cases Based on Apriori Algorithm , 2019, ICMAI 2019.

[16]  Lahcen Oughdir,et al.  Association Rules Mining Method of Big Data for E-Learning Recommendation Engine , 2018, Advances in Intelligent Systems and Computing.

[17]  Habiba Drias,et al.  Association Rule Mining Based on Bat Algorithm , 2015, BIC-TA.

[18]  Ruowu Zhong,et al.  Research of Commonly Used Association Rules Mining Algorithm in Data Mining , 2011, 2011 International Conference on Internet Computing and Information Services.

[19]  R.Santhi,et al.  USING HASH BASED APRIORI ALGORITHM TO REDUCE THE CANDIDATE 2- ITEMSETS FOR MINING ASSOCIATION RULE , 2011 .

[20]  Zhigang Lu,et al.  An Improved Apriori-based Algorithm for Association Rules Mining , 2009, 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery.

[21]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques with Java implementations , 2002, SGMD.

[22]  Ian H. Witten,et al.  WEKA: a machine learning workbench , 1994, Proceedings of ANZIIS '94 - Australian New Zealnd Intelligent Information Systems Conference.

[23]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[24]  Santosh V. Chobe,et al.  An Overview of Association Rule Mining Algorithms , 2014 .

[25]  Jaishree Singh,et al.  Improving Efficiency of Apriori Algorithm Using Transaction Reduction , 2013 .

[26]  Jiao Yabing,et al.  Research of an Improved Apriori Algorithm in Data Mining Association Rules , 2013 .

[27]  Sanjeev Rao,et al.  Implementing Improved Algorithm Over APRIORI Data Mining Association Rule Algorithm , 2012 .

[28]  Yosef Hasan Jbara,et al.  An Improved Algorithm for Mining Association Rules in Large Databases , 2011 .