Data Collected and Analysis of COVID-19 Infection Status: Case Study of Iraqi Hospital Patients in Diwaniyah and Najaf Governorates : Data Collected and Analysis of COVID-19 Infection Status: Case Study of Iraqi Hospital Patients in Diwaniyah and Najaf Governorates

Haitham Abdulaaima; Ali Hasan Taresh

doi:10.31185/wjps.173

Authors

Haitham Abdulaaima Aswad
Ali Hasan Taresh

DOI:

https://doi.org/10.31185/wjps.173

Keywords:

COVID-19 Dataset; Data analysis; Electronic documentation; Machine learning; information retrieval.

Abstract

In recent years, the coronavirus (COVID-19) spread in a dangerous and rapidly. It first appeared in the Chinese city of Wuhan in early December 2019, causing many cases of infection and death because of the rapid spread of the virus throughout the world. The conversion of patient information from paper to electronic helps the medical staff and researchers analyze and retrieve information faster. In this paper, we gathered data about patients infected with COVID-19 who were registered in the Iraqi hospitals of two governorates (Diwaniyah and Najaf) from 2020 to 2023. In addition, we used machine learning algorithms that protect the infection of the data entered for the patient. The highest accuracy (average in both datasets od Diwaniyah and Najaf) ) are achieved by both the algorithms Nave Bayes (90%) and Decision tree (90%) , while the K nearest neighbor (89%), Random forest() and artificial neural network (84).

References

S. S. Mohamed Ali, A. H. Alsaeedi, D. Al-Shammary, H. H. Alsaeedi, and H. W. Abid, “Efficient intelligent system for diagnosis pneumonia (SARSCOVID19) in X-ray images empowered with initial clustering,” Indones. J. Electr. Eng. Comput. Sci., vol. 22, no. 1, pp. 241–251, 2021, doi: 10.11591/ijeecs.v22.i1.pp241-251.

X. Wang, T. Okoshi, and J. Nakazawa, “Lower Face Inpainting Aiming at Face Recognition under Occlusions,” 2022 IEEE Int. Conf. Pervasive Comput. Commun. Work. other Affil. Events, PerCom Work. 2022, pp. 62–65, 2022, doi: 10.1109/PerComWorkshops53856.2022.9767220.

S. S. Mohamed Ali, A. H. Alsaeedi, D. Al-Shammary, H. H. Alsaeedi, and H. W. Abid, “Efficient intelligent system for diagnosis pneumonia (SARSCOVID19) in X-ray images empowered with initial clustering,” Indones. J. Electr. Eng. Comput. Sci., vol. 22, no. 1, pp. 241–251, 2021, doi: 10.11591/ijeecs.v22.i1.pp241-251.

V. Aswal, O. Tupe, S. Shaikh, and N. N. Charniya, “Single Camera Masked Face Identification,” Proc. - 19th IEEE Int. Conf. Mach. Learn. Appl. ICMLA 2020, pp. 57–60, 2020, doi: 10.1109/ICMLA51294.2020.00018.

A. H. Al-saeedi, “Binary Mean-Variance Mapping Optimization Algorithm (BMVMO),” J. Appl. Phys. Sci., vol. 2, no. 2, pp. 42–47, 2016, doi: 10.20474/japs-2.2.3.

S. Nematzadeh, F. Kiani, M. Torkamanian-Afshar, and N. Aydin, “Tuning hyperparameters of machine learning algorithms and deep neural networks using metaheuristics: A bioinformatics study on biomedical and biological cases,” Comput. Biol. Chem., vol. 97, p. 107619, 2022, doi: https://doi.org/10.1016/j.compbiolchem.2021.107619.

R. R. Nuiaa, S. Manickam, and A. H. Alsaeedi, “A Comprehensive Review of DNS-based Distributed Reflection Denial of Service (DRDoS) Attacks: State-of-the-Art,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 12, no. 6, pp. 2452–2461, 2022, doi: 10.18517/ijaseit.12.6.17280.

Y. Shi et al., “An overview of COVID-19,” J. Zhejiang Univ. Sci. B, vol. 21, no. 5, p. 343, 2020.

B. F. Haynes et al., “Prospects for a safe COVID-19 vaccine,” Sci. Transl. Med., vol. 12, no. 568, p. eabe0948, 2020.

S. J. Daniel, “Education and the COVID-19 pandemic,” Prospects, vol. 49, no. 1, pp. 91–96, 2020.

C. Wang, Z. Li, M. Clay Mathews, S. Praharaj, B. Karna, and P. Solís, “The spatial association of social vulnerability with COVID-19 prevalence in the contiguous United States,” Int. J. Environ. Health Res., vol. 32, no. 5, pp. 1147–1154, 2022.

F. Saberi-Movahed et al., “Decoding clinical biomarker space of COVID-19: Exploring matrix factorization-based feature selection methods,” Comput. Biol. Med., vol. 146, pp. 1–51, 2022, doi: 10.1016/j.compbiomed.2022.105426.

M. Loey, G. Manogaran, M. H. N. Taha, and N. E. M. Khalifa, “A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic,” Meas. J. Int. Meas. Confed., vol. 167, no. May 2020, 2021, doi: 10.1016/j.measurement.2020.108288.

S. M. Hadi et al., “Trigonometric words ranking model for spam message classification,” IET Networks, no. August, 2022, doi: 10.1049/ntw2.12063.

D. Al-Shammary, A. L. Albukhnefis, A. H. Alsaeedi, and M. Al-Asfoor, “Extended particle swarm optimization for feature selection of high-dimensional biomedical data,” Concurr. Comput. Pract. Exp., vol. 34, no. 10, p. e6776, 2022, doi: https://doi.org/10.1002/cpe.6776.

V. Meshram, K. Patil, V. Meshram, D. Hanchate, and S. D. Ramkteke, “Machine learning in agriculture domain: A state-of-art survey,” Artif. Intell. Life Sci., vol. 1, no. August, p. 100010, 2021, doi: 10.1016/j.ailsci.2021.100010.

A. H. Jabor and A. H. Ali, “Dual Heuristic Feature Selection Based on Genetic Algorithm and Binary Particle Swarm Optimization,” J. Univ. BABYLON Pure Appl. Sci., vol. 27, no. 1, pp. 171–183, 2019, doi: 10.29196/jubpas.v27i1.2106.

R. R. Nuiaa, S. Manickam, and A. H. Alsaeedi, “Distributed reflection denial of service attack: A critical review,” Int. J. Electr. Comput. Eng., vol. 11, no. 6, pp. 5327–5341, 2021, doi: 10.11591/ijece.v11i6.pp5327-5341.

O. Almomani, “A feature selection model for network intrusion detection system based on pso, gwo, ffa and ga algorithms,” Symmetry (Basel)., vol. 12, no. 6, pp. 1–20, 2020, doi: 10.3390/sym12061046.

S. Manickam, R. Rahef Nuiaa, A. Hakem Alsaeedi, Z. A. A. Alyasseri, M. A. Mohammed, and M. M. Jaber, “An enhanced mechanism for detection of Domain Name System‐based distributed reflection denial of service attacks depending on modified metaheuristic algorithms and adaptive thresholding techniques,” IET Networks, vol. 11, no. 5, pp. 169–181, 2022.

A. Bassel, A. B. Abdulkareem, Z. A. A. Alyasseri, N. S. Sani, and H. J. Mohammed, “Automatic Malignant and Benign Skin Cancer Classification Using a Hybrid Deep Learning Approach,” Diagnostics, vol. 12, no. 10, 2022, doi: 10.3390/diagnostics12102472.

A. S. Alfoudi et al., “Hyper clustering model for dynamic network intrusion detection,” IET Commun., 2022, doi: 10.1049/cmu2.12523.

S. M. Hadi, A. H. Alsaeedi, M. I. Dohan, R. R. Nuiaa, S. Manickam, and A. S. D. Alfoudi, “Dynamic Evolving Cauchy Possibilistic Clustering Based on the Self-Similarity Principle (DECS) for Enhancing Intrusion Detection System,” Int. J. Intell. Eng. Syst., vol. 15, no. 5, pp. 252–260, 2022, doi: 10.22266/ijies2022.1031.23.

A. S. Alfoudi, A. H. Alsaeedi, M. H. Abed, A. M. Otebolaku, and Y. S. Razooqi, “Palm Vein Identification Based on Hybrid Feature Selection Model,” Int. J. Intell. Eng. Syst., vol. 14, no. 5, pp. 469–478, 2021, doi: 10.22266/ijies2021.1031.41.

R. R. Nuiaa, S. Manickam, A. H. Alsaeedi, and E. S. Alomari, “A new proactive feature selection model based on the enhanced optimization algorithms to detect DRDoS attacks,” Int. J. Electr. Comput. Eng., vol. 12, no. 2, pp. 1869–1880, 2022, doi: 10.11591/ijece.v12i2.pp1869-1880.

A. L. A. A. H. A. A. H. A. Mehdi Ebady Manna, “A proactive metaheuristic model for optimizing weights of artificial neural network,” Indones. J. Electr. Eng. Comput. Sci., vol. 20, no. 2, pp. 976–984, 2020, doi: 10.11591/ijeecs.v20.i2.pp976-984.

P. Boonyopakorn, “The Optimization and Enhancement of Network Intrusion Detection through Fuzzy Association Rules,” 2019 6th Int. Conf. Tech. Educ. ICTechEd6 2019, no. August, 2019, doi: 10.1109/ICTechEd6.2019.8790881.

Data Collected and Analysis of COVID-19 Infection Status: Case Study of Iraqi Hospital Patients in Diwaniyah and Najaf Governorates