Coronavirus Disease Diagnosis, Care and Prevention (COVID-19) Based on Decision Support System

Main Article Content

Hussein Ali Salah
Ahmed Shihab Ahmed


              Automated clinical decision support system (CDSS) acts as new paradigm in medical services today. CDSSs are utilized to increment specialists (doctors) in their perplexing decision-making. Along these lines, a reasonable decision support system is built up dependent on doctors' knowledge and data mining derivation framework so as to help with the interest the board in the medical care gracefully to control the Corona Virus Disease (COVID-19) virus pandemic and, generally, to determine the class of infection and to provide a suitable protocol treatment depending on the symptoms of patient. Firstly, it needs to determine the three early symptoms of COVID-19 pandemic criteria (fever, tiredness, dry cough and breathing difficulty) used to diagnose the person being infected by COVID-19 virus or not. Secondly, this approach divides the infected peoples into four classes, based on their immune system risk level (very high degree, high degree, mild degree, and normal), and using two indices of age and current health status like diabetes, heart disorders, or hypertension. Where, these people are graded and expected to comply with their class regulations. There are six important COVID-19 virus infections of different classes that should receive immediate health care to save their lives. When the test is positive, the patient age is considered to choose one of the six classifications depending on the patient symptoms to provide him the suitable care as one of the four types of suggested treatment protocol of COVID-19 virus infection in COVID-19 DSS application. Finally, a report of all information about any classification case of COVID-19 infection is printed where this report includes the status of patient (infection level) and the prevention protocol. Later, the program sends the report to the control centre (medical expert) containing the information. In this paper, it was suggested the use of C4.5 Algorithm for decision tree.


Download data is not yet available.

Article Details

How to Cite
Salah HA, Ahmed AS. Coronavirus Disease Diagnosis, Care and Prevention (COVID-19) Based on Decision Support System. Baghdad Sci.J [Internet]. [cited 2021Mar.4];18(3):0593. Available from:


World Health organization. Coronavirus Disease (COVID-19). 2020. 2020.

Squizzato A, Donadini MP, Galli L, Dentali F, Aujesky D, Ageno W. Prognostic clinical prediction rules to identify a low‐risk pulmonary embolism: a systematic review and meta‐analysis. J Thromb Haemost. 2012 Jul;10(7):1276-90.

Sutton RT, Pincock D, Baumgart DC, Sadowski DC, Fedorak RN, Kroeker KI. An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digit Med. 2020 Feb 6;3(1):1-0.

Moja L, Friz HP, Capobussi M, Kwag K, Banzi R, Ruggiero F, et al. Effectiveness of a Hospital-Based Computerized Decision Support System on Clinician Recommendations and Patient Outcomes: A Randomized Clinical Trial. JAMA Netw Open. 2019 Dec 2;2(12):e1917094-.

Clarke SM, Griebsch JH, Simpson TW. Analysis of Support Vector Regression for Approximation of Complex Engineering Analyses. InInternational Design Engineering Technical Conferences and Computers and Information in Engineering Conference 2003 Jan 1 ,Vol. 37009, pp. 535-543).

Kumar DS, Sathyadevi G, Sivanesh S. Decision support system for medical diagnosis using data mining. IJCSI. 2011 May 1;8(3):147.

Shortliffe EH, Blois MS. The computer meets medicine and biology: emergence of a discipline. InBiomedical informatics 2006 (pp. 3-45). Springer, New York, NY.

Liu Y, Wang Z, Ren J, Tian Y, Zhou M, Zhou T, et al. A COVID-19 risk assessment decision support system for general practitioners: design and development study. JMIR. 2020;22(6):e19786.

McRae MP, Dapkins IP, Sharif I, Anderman J, Fenyo D, Sinokrot O, et al. Managing COVID-19 With a Clinical Decision Support Tool in a Community Health Network: Algorithm Development and Validation. JMIR. 2020;22(8):e22033.

Qjidaa M, Ben-Fares A, Mechbal Y, Amakdouf H, Maaroufi M, Alami B, et al. Development of a clinical decision support system for the early detection of COVID-19 using deep learning based on chest radiographic images. In2020 International Conference on Intelligent Systems and Computer Vision (ISCV) 2020 Jun 9 (pp. 1-6). IEEE.

Wu G, Yang P, Xie Y, Woodruff HC, Rao X, Guiot J, et al. Development of a clinical decision support system for severity risk prediction and triage of COVID-19 patients at hospital admission: an international multicentre study. ERJ Open Res. 2020 Aug 1;56(2).

Alqahtani SS, Alshahri S, Almaleh AI, Nadeem F. The implementation of clinical decision support system: a case study in Saudi Arabia. IJ Information Technology and Computer Science. 2016:23-30.

Cheung DS, Grubenhoff JA. Machine learning in clinical medicine still finding its way. JAMA Netw Open. 2019 Jan 4;2(1):e186926-.

Dous ZM, Sewisy AA, Seddik MF. Decision making techniques and tools based on decision support system. IJERA. 2018 Mar;8:9-16.

Bezemer T, De Groot MC, Blasse E, Ten Berg MJ, Kappen TH, Bredenoord AL, et al. A human (e) factor in clinical decision support systems. JMIR. 2019;21(3):e11732.

Dinevski D, Bele U, Šarenac T, Rajkovič U, Šušteršič O. Clinical decision support systems. Telemedicine Techniques and Applications.Intech Open. 2011 Jun 20:185-210.

Banerjee I, Sofela M, Yang J, Chen JH, Shah NH, Ball R, et al. Development and performance of the pulmonary embolism result forecast model (PERFORM) for computed tomography clinical decision support. JAMA Netw Open. 2019 Aug 2;2(8):e198719-.

Coiera E. Guide to health informatics. CRC press; 2015 Mar 6.

Holyoak KJ, Morrison RG, editors. The Cambridge handbook of thinking and reasoning. Cambridge: Cambridge University Press; 2005 Apr.

Goto T, Camargo CA, Faridi MK, Freishtat RJ, Hasegawa K. Machine learning–based prediction of clinical outcomes for children during emergency department triage. JAMA Netw Open. 2019 Jan 4;2(1):e186937-.

Gamboa AL, Mendoza MG, Orozco RE, Vargas JM, Gress NH. Hybrid fuzzy-SV clustering for heart disease identification. In2006 International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce (CIMCA'06) 2006 Nov 28; (pp. 121-121). IEEE.

Polat K, Güneş S. An expert system approach based on principal component analysis and adaptive neuro-fuzzy inference system to diagnosis of diabetes disease. Digital Signal Processing (DSP). 2007 Jul 1;17(4):702-10.

Abumelha M, Hashbal A, Nadeem F, Aljohani N. Development of infection control surveillance system for intensive care unit: data requirements and guidelines. IJISA. 2016 Jun 1;8(6):19.

Berner ES. Clinical decision support systems. New York: Springer Science+ Business Media, LLC; 2007.

Esmaeilzadeh P, Sambasivan M, Kumar N, Nezakati H. Adoption of clinical decision support systems in a developing country: Antecedents and outcomes of physician's threat to perceived professional autonomy. Int J Med Inform. 2015 Aug 1;84(8):548-60.

Sanchez E, Toro C, Artetxe A, Graña M, Sanin C, Szczerbicki E, et al. Bridging challenges of clinical decision support systems with a semantic approach. A case study on breast cancer. Pattern Recognit Lett. 2013 Oct 15;34(14):1758-68.

Molina-Ruiz HD, García-Munguía M, García-Vargas MD, Carreón-Guillén J, García-Lirios C. Una aproximación estadística al comportamiento de brote de COVID-19 en la China continental. TEPEXI Boletín Científico de la Escuela Superior Tepeji del Río. 2020 Jul 5;7(14):6-16.

Qarawi AT, Ng SJ, Gad A, Mai LN, AL-Ahdal TM, Sharma A, et al. Awareness and Preparedness of Hospital Staff against Novel Coronavirus (COVID-2019): A Global Survey-Study Protocol.

World Health Organization. Coronavirus disease (COVID‐19) advice for the public [website]. 2020.

ul Qamar MT, Alqahtani SM, Alamri MA, Chen LL. Structural basis of SARS-CoV-2 3CLpro and anti-COVID-19 drug discovery from medicinal plants. J Pharm Anal. 2020 Mar 26.

Novel CP. The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19) in China. Zhonghua liu xing bing xue za zhi= Zhonghua liuxingbingxue zazhi. 2020 Feb 17;41(2):145.

Jin Z, Du X, Xu Y, Deng Y, Liu M, Zhao Y, et al. Structure-based drug design, virtual screening and high-throughput screening rapidly identify antiviral leads targeting COVID-19. BioRxiv. 2020 Jan 1.

Mohammed MA, Abdulkareem KH, Mostafa SA, Ghani MK, Maashi MS, Garcia-Zapirain B, et al. Voice Pathology Detection and Classification Using Convolutional Neural Network Model. Applied Sciences(MDPI). 2020 Jan;10(11):3723.

Al-Dhief FT, Latiff NM, Malik NN, Salim NS, Baki MM, Albadr MA, et al. A Survey of Voice Pathology Surveillance Systems Based on Internet of Things and Machine Learning Algorithms. IEEE Access. 2020 Apr 1;8:64514-33.

Mohammed MA, Abdulkareem KH, Al-Waisy AS, Mostafa SA, Al-Fahdawi S, Dinar AM, et al. Benchmarking Methodology for Selection of Optimal COVID-19 Diagnostic Model Based on Entropy and TOPSIS Methods. IEEE Access. 2020 May 19.

Wang W, Xu Y, Gao R, Lu R, Han K, Wu G, et al. Detection of SARS-CoV-2 in different types of clinical specimens. JAMA Netw Open. 2020 May 12;323(18):1843-4.

Ng MY, Lee EY, Yang J, Yang F, Li X, Wang H, et al. Imaging profile of the COVID-19 infection: radiologic findings and literature review. Radiology: Cardiothoracic Imaging. 2020 Feb 13;2(1):e200034.

Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Yet al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The lancet. 2020 Feb 15;395(10223):497-506.

Ai T, Yang Z, Hou H, Zhan C, Chen C, Lv W, et al. Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology. 2020 Feb 26:200642.

Fang Y, Zhang H, Xie J, Lin M, Ying L, Pang P, et al. Sensitivity of chest CT for COVID-19: comparison to RT-PCR. Radiology. 2020 Feb 19:200432.

McRae MP, Simmons GW, Christodoulides NJ, Lu Z, Kang SK, Fenyo D, et al. Clinical decision support tool and rapid point-of-care platform for determining disease severity in patients with COVID-19. Lab on a Chip. 2020.

Güler M, Geçici E. A decision support system for scheduling the shifts of physicians during COVID-19 pandemic. Comput Ind Eng. 2020 Sep 25;150:106874-.

Figen S, Murat S, Tamer S. A Clinical Decision Support System for the Treatment of COVID-19 with Multi-Criteria Decision-Making Techniques: JMIR Med Inform.2020.

Govindan K, Mina H, Alavi B. A decision support system for demand management in healthcare supply chains considering the epidemic outbreaks: A case study of coronavirus disease 2019 (COVID-19). Transp Res E Logist Transp Rev. 2020 Jun 1;138:101967.

Shen K, Yang Y, Wang T, Zhao D, Jiang Y, Jin R, et al. Diagnosis, treatment, and prevention of 2019 novel coronavirus infection in children: experts’ consensus statement. World J Pediatr. 2020 Feb 7:1-9.

Beck BR, Shin B, Choi Y, Park S, Kang K. Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model. Comput Struct Biotechnol J. 2020 Jan 1;18:784-90.

Xu X, Chen P, Wang J, Feng J, Zhou H, Li X, et al. Evolution of the novel coronavirus from the ongoing Wuhan outbreak and modeling of its spike protein for risk of human transmission. Sci China Life Sci. 2020 Mar;63(3):457-60.

Yanli XU, Ying LI, Chen Y, Xin S, Xie L, Liang Y, et al. A multicenter controlled clinical study on the efficacy and safety of recombinant human interferon α2b spray in the treatment of hand, foot and mouth disease in children. Chinese Journal of Infectious Diseases(Zhonghua Chuan Ran Bing Za Zhi). 2018 Jan 1;36(2):101-6.

Zhu N, Zhang D, Wang W, Li X, Yang B, Song J, et al. A Novel Coronavirus from Patients with Pneumonia in China, 2019. N Engl J Med. 2020 Feb 20;382:727-33.

Shen KL, Shang YX, Zhang H. A multicenter, randomized, controlled clinical study on the efficacy and safety of recombinant human interferon 2b spray (pseudomonas) in the treatment of acute upper respiratory tract infection in children. Chin J Appl Clin Pediatr. 2019;34:1010-6.

Ang L, Lee HW, Choi JY, Zhang J, Lee MS. Herbal medicine and pattern identification for treating COVID-19: a rapid review of guidelines. Integrative Medicine Research. 2020 Mar 29:100407.

Hafeez A, Ahmad S, Siddqui SA, Ahmad M, Mishra S. A Review of COVID-19 (Coronavirus Disease-2019) Diagnosis. Treatments Prevention. 2019.

Aziz PY, Hadi JM, Aram MS, Aziz SB, Rahman HS, Ahmed HA, et al. The strategy for controlling COVID-19 in Kurdistan Regional Government (KRG)/Iraq: identification, epidemiology, transmission, treatment, and recovery. IJS open. 2020 Jan 1;25:41-6.

Wu F, Zhao S, Yu B, Chen YM, Wang W, Song ZG, et al. A new coronavirus associated with human respiratory disease in China. Nature. 2020 Mar;579(7798):265-9.

Michaels D, Wagner GR. Occupational Safety and Health Administration (OSHA) and worker safety during the COVID-19 pandemic. JAMA Netw Open. 2020 Sep 16.

Summers K, McCullough M, Smith E, Gwinn M, Kremer F, Sjogren M, et al. The sustainable and healthy communities research program: the Environmental Protection Agency’s research approach to assisting community decision-making. Sustainability. 2014 Jan;6(1):306-18.

Foster D, McGregor C, El-Masri S. A survey of agent-based intelligent decision support systems to support clinical management and research. Inproceedings of the 2nd international workshop on multi-agent systems for medicine, computational biology, and bioinformatics 2005 Jul 25 (pp. 16-34).

Russell SJ, Norvig P. Artificial Intelligence A Modern Approach; PearsonEducation. Artificial Intelligence: A Modern Approach: Pearson Education. 2003.