Employing data mining techniques to classify Covid-19 pandemic
Recently, researchers and clinicians have been searching for new technologies to slow down or stop COVID-19 pandemic. The utility of Data Mining (DM) algorithms to suggests new opportunities to combat the spread of the new Coronavirus. This paper suggests a comparative study on data mining approache...
Saved in:
| Published in | AIP conference proceedings Vol. 3036; no. 1 |
|---|---|
| Main Authors | , , |
| Format | Journal Article Conference Proceeding |
| Language | English |
| Published |
Melville
American Institute of Physics
15.03.2024
|
| Subjects | |
| Online Access | Get full text |
| ISSN | 0094-243X 1935-0465 1551-7616 1551-7616 |
| DOI | 10.1063/5.0196328 |
Cover
| Abstract | Recently, researchers and clinicians have been searching for new technologies to slow down or stop COVID-19 pandemic. The utility of Data Mining (DM) algorithms to suggests new opportunities to combat the spread of the new Coronavirus. This paper suggests a comparative study on data mining approaches to predict COVID19. We used common classification algorithms like the Support Vector Machines, Random Forest, Logistic Regression, K-Nearest Neighbor and Artificial Neural Network with Python simulation to compare it in metrics accuracy, recall, precision and AUC; results showed that Random Forest model had a 98.43% accuracy – which is a higher accuracy than many other previous studies known COVID-19 data mining algorithms. |
|---|---|
| AbstractList | Recently, researchers and clinicians have been searching for new technologies to slow down or stop COVID-19 pandemic. The utility of Data Mining (DM) algorithms to suggests new opportunities to combat the spread of the new Coronavirus. This paper suggests a comparative study on data mining approaches to predict COVID19. We used common classification algorithms like the Support Vector Machines, Random Forest, Logistic Regression, K-Nearest Neighbor and Artificial Neural Network with Python simulation to compare it in metrics accuracy, recall, precision and AUC; results showed that Random Forest model had a 98.43% accuracy – which is a higher accuracy than many other previous studies known COVID-19 data mining algorithms. |
| Author | Bouchakwa, Mariam Shanshool, Abeer M. Amor, Ikram Amous-Ben |
| Author_xml | – sequence: 1 givenname: Abeer M. surname: Shanshool fullname: Shanshool, Abeer M. organization: National School of Electronics and Communications, Sfax university – sequence: 2 givenname: Mariam surname: Bouchakwa fullname: Bouchakwa, Mariam email: mariam.bouchekwa@gmail.com organization: National School of Electronics and Communications, Sfax university – sequence: 3 givenname: Ikram Amous-Ben surname: Amor fullname: Amor, Ikram Amous-Ben email: ikram.amous@enetcom.usf.tn organization: Higher Institute of Applied Sciences and Technology of Sousse |
| BookMark | eNp9j09LwzAchoNMcJse_AYFb0JnfmnSpBdBxqbCwIuCt5Dmj2a0SW06pd_ejQ28eXrfw8vD-8zQJMRgEboGvABcFndsgaEqCyLO0BQYg5yXUE7QFOOK5oQW7xdoltIWY1JxLqboftV2TRx9-MiMGlTW-nDog9WfwX_tbMqGmOlGpeTdmC3jtzc5VFmngrGt15fo3Kkm2atTztHbevW6fMo3L4_Py4dN3kEpRG54aTQx2tC64MzV1jgAxyhW3FBlBLPEcqWcAE2h1ooC5gBGs5pRYpkr5uj2yN2FTo0_qmlk1_tW9aMELA_mksmT-X58cxx3fTwoDHIbd33Y_5OkYpxxgQv8h0zaD2rwMfyD_AUeqGZ7 |
| CODEN | APCPCS |
| ContentType | Journal Article Conference Proceeding |
| Copyright | Author(s) 2024 Author(s). Published by AIP Publishing. |
| Copyright_xml | – notice: Author(s) – notice: 2024 Author(s). Published by AIP Publishing. |
| DBID | 8FD H8D L7M ADTOC UNPAY |
| DOI | 10.1063/5.0196328 |
| DatabaseName | Technology Research Database Aerospace Database Advanced Technologies Database with Aerospace Unpaywall for CDI: Periodical Content Unpaywall |
| DatabaseTitle | Technology Research Database Aerospace Database Advanced Technologies Database with Aerospace |
| DatabaseTitleList | Technology Research Database |
| Database_xml | – sequence: 1 dbid: UNPAY name: Unpaywall url: https://proxy.k.utb.cz/login?url=https://unpaywall.org/ sourceTypes: Open Access Repository |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Physics |
| EISSN | 1551-7616 |
| Editor | Obaid, Ahmed J. Abid, Salah Hamza EIdi, Jaafer Hmood |
| Editor_xml | – sequence: 1 givenname: Ahmed J. surname: Obaid fullname: Obaid, Ahmed J. organization: University of Kufa – sequence: 2 givenname: Salah Hamza surname: Abid fullname: Abid, Salah Hamza organization: Mustansiriyah University – sequence: 3 givenname: Jaafer Hmood surname: EIdi fullname: EIdi, Jaafer Hmood organization: Mustansiriyah University |
| ExternalDocumentID | 10.1063/5.0196328 acp |
| Genre | Conference Proceeding |
| GroupedDBID | -~X 23M 5GY AAAAW AABDS AAEUA AAPUP AAYIH ABJNI ACBRY ACZLF ADCTM AEJMO AFATG AFHCQ AGKCL AGLKD AGMXG AGTJO AHSDT AJJCW ALEPV ALMA_UNASSIGNED_HOLDINGS ATXIE AWQPM BPZLN F5P FDOHQ FFFMQ HAM M71 M73 RIP RQS SJN ~02 8FD ABJGX ADMLS H8D L7M 0ZJ ADTOC J23 NEUPN RDFOP UNPAY |
| ID | FETCH-LOGICAL-p1688-d76dc2dcd4b375fbedf11f540a7d4ad85e2e7aaf81c41bca410711dc5b542e5f3 |
| IEDL.DBID | UNPAY |
| ISSN | 0094-243X 1935-0465 1551-7616 |
| IngestDate | Tue Aug 19 23:17:41 EDT 2025 Sun Jun 29 15:33:03 EDT 2025 Fri Jun 21 00:11:00 EDT 2024 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Language | English |
| License | 0094-243X/2024/3036/030001/13/$30.00 Published by AIP Publishing. |
| LinkModel | DirectLink |
| MeetingName | 4TH INTERNATIONAL CONFERENCE ON PURE SCIENCES: ICPS2023 |
| MergedId | FETCHMERGED-LOGICAL-p1688-d76dc2dcd4b375fbedf11f540a7d4ad85e2e7aaf81c41bca410711dc5b542e5f3 |
| Notes | ObjectType-Conference Proceeding-1 SourceType-Conference Papers & Proceedings-1 content type line 21 |
| OpenAccessLink | https://proxy.k.utb.cz/login?url=https://pubs.aip.org/aip/acp/article-pdf/doi/10.1063/5.0196328/19796900/030001_1_5.0196328.pdf |
| PQID | 2957578030 |
| PQPubID | 2050672 |
| PageCount | 13 |
| ParticipantIDs | scitation_primary_10_1063_5_0196328 unpaywall_primary_10_1063_5_0196328 proquest_journals_2957578030 |
| PublicationCentury | 2000 |
| PublicationDate | 20240315 |
| PublicationDateYYYYMMDD | 2024-03-15 |
| PublicationDate_xml | – month: 03 year: 2024 text: 20240315 day: 15 |
| PublicationDecade | 2020 |
| PublicationPlace | Melville |
| PublicationPlace_xml | – name: Melville |
| PublicationTitle | AIP conference proceedings |
| PublicationYear | 2024 |
| Publisher | American Institute of Physics |
| Publisher_xml | – name: American Institute of Physics |
| References | Huyut (c17) 2022 Rajaraman, Siegelman, Alderson, Folio, Folio, Antani (c14) 2020 Simsek, Kursuncu, Kibis, AnisAbdellatif, Dag (c22) 2020 Muhammad, Islam, Usman, Ayon (c12) 2020 Chaurasia, Pal (c11) 2020 Sear, Velásquez, Leahy, Restrepo, El Oud, Gabriel, Lupu, Johnson (c24) 2020 Shalev-Shwartz, Singer, Srebro, Cotter (c28) 2011 Akhtar (c16) 2021 Chamola, Hassija, Gupta, Guizani (c4) 2020 Tuli, Tuli, Tuli, Gill (c13) 2020 Ribeiro, Pires, de Moura Oliveira (c19) 2019 Jamshidi, Lalbakhsh, Talla, Peroutka, Hadjilooei, Lalbakhsh, Jamshidi, La Spada, Mirmozafari, Dehghani, Sabet (c15) 2020 Pham (c30) 2021 Keleş (c18) 2019 Kiruthika, Raja, Jaichandran, Priyadharshini (c10) 2019 Rahman, Islam, Manik, Hossen, Al-Rakhami (c9) 2021 Golhani, Balasundram, Vadamalai, Pradhan (c27) 2018 Bergstra, Komer, Eliasmith, Yamins, Cox (c25) 2015 Gao, Mei, Piccialli, Cuomo, Tu, Huo (c29) 2020 Dairi, Harrou, Zeroual, Hittawe, Sun (c8) 2021 Abeer, Saeed, Rafash, Salman (c5) 2022 Muhammad, Algehyne, Usman, Ahmad, Chakraborty, Mohammed (c7) 2021 Jonathan, Anjali (c31) 2021 Breiman (c26) 2001 Kwekha-Rashid, Abduljabbar, Alhayani (c2) 2021 Alabrah, Alawadh, Okon, Meraj, Rauf (c6) 2022 Amar, Taha, Mohamed (c3) 2020 |
| References_xml | – year: 2022 ident: c17 article-title: Automatic Detection of Severely and Mildly Infected COVID-19 Patients with Supervised Machine Learning Models – start-page: 149 year: 2019 ident: c18 article-title: Breast cancer prediction and detection using data mining classification algorithms: a comparative study – start-page: 3 year: 2011 ident: c28 article-title: Pegasos: Primal estimated sub-gradient solver for svm – start-page: 467 year: 2022 ident: c6 article-title: Gulf countries’ citizens’ acceptance of COVID-19 vaccines—A machine learning approach – start-page: 014008 year: 2015 ident: c25 article-title: Hyperopt: a python library for model selection and hyperparameter optimization – start-page: 100254 year: 2020 ident: c29 article-title: Julia language in machine learning: Algorithms, applications, and open issues – start-page: 1 year: 2020 ident: c11 article-title: Application of machine learning time series analysis for prediction COVID-19 pandemic – start-page: 115041 year: 2020 ident: c14 article-title: Iteratively pruned deep learning ensembles for COVID-19 detection in chest X-rays – start-page: 622 year: 2020 ident: c3 article-title: Prediction of the final size for COVID-19 epidemic using machine learning: A case study of Egypt – start-page: 1 year: 2019 ident: c19 article-title: Breast Cancer Diagnosis using a Neural Network publication-title: 2019 IEEE 6th Portuguese Meeting on Bioengineering (ENBENG) – start-page: 91886 year: 2020 ident: c24 article-title: Quantifying COVID-19 content in the online health opinion war using machine learning – start-page: 354 year: 2018 ident: c27 article-title: A review of neural networks in plant disease detection using hyperspectral data – start-page: 112863 year: 2020 ident: c22 article-title: A hybrid data mining approach for identifying the temporal effects of variables associated with breast cancer survival publication-title: Expert Systems with Applications – start-page: 1 year: 2020 ident: c12 article-title: Predictive data mining models for novel coronavirus (COVID-19) infected patients’ recovery – start-page: 5 year: 2001 ident: c26 article-title: Random forests – start-page: 90225 year: 2020 ident: c4 article-title: A comprehensive review of the COVID-19 pandemic and the role of IoT, drones, AI, blockchain, and 5G in managing its impact – start-page: 1 year: 2021 ident: c2 article-title: Coronavirus disease (COVID-19) cases analysis using machine-learning applications – start-page: 1 year: 2021 ident: c7 article-title: Supervised machine learning models for prediction of COVID-19 infection using epidemiology dataset – start-page: 65 year: 2021 ident: c16 article-title: COVID-19 detection from CBC using machine learning techniques – start-page: 109581 year: 2020 ident: c15 article-title: Artificial intelligence and COVID-19: deep learning approaches for diagnosis and treatment – start-page: 1 year: 2021 ident: c30 article-title: Classification of COVID-19 chest X-rays with deep learning: new models or fine tuning? – start-page: 431 year: 2022 ident: c5 article-title: A review study on machine learning approaches on coronavirus big data – start-page: 103791 year: 2021 ident: c8 article-title: Comparative study of machine learning methods for COVID-19 transmission forecasting – start-page: 2277 year: 2019 ident: c10 article-title: Detection and Classification of Paddy Crop Disease using Deep Learning Techniques – start-page: 100222 year: 2020 ident: c13 article-title: Predicting the growth and trend of COVID-19 pandemic using machine learning and cloud computing – start-page: 73 year: 2021 ident: c31 article-title: Analysis of Influencing Risk Factors for Covid-19 Infection Based on the Predictive Models Using Machine Learning Algorithms – start-page: 1 year: 2021 ident: c9 article-title: Machine learning approaches for tackling novel coronavirus (COVID-19) pandemic |
| SSID | ssj0029778 |
| Score | 2.3515449 |
| Snippet | Recently, researchers and clinicians have been searching for new technologies to slow down or stop COVID-19 pandemic. The utility of Data Mining (DM)... |
| SourceID | unpaywall proquest scitation |
| SourceType | Open Access Repository Aggregation Database Publisher |
| SubjectTerms | Accuracy Algorithms Artificial neural networks Comparative studies Data mining New technology Support vector machines |
| Title | Employing data mining techniques to classify Covid-19 pandemic |
| URI | http://dx.doi.org/10.1063/5.0196328 https://www.proquest.com/docview/2957578030 https://pubs.aip.org/aip/acp/article-pdf/doi/10.1063/5.0196328/19796900/030001_1_5.0196328.pdf |
| UnpaywallVersion | publishedVersion |
| Volume | 3036 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVEBS databaseName: Inspec with Full Text customDbUrl: eissn: 1551-7616 dateEnd: 20241101 omitProxy: false ssIdentifier: ssj0029778 issn: 0094-243X databaseCode: ADMLS dateStart: 20000101 isFulltext: true titleUrlDefault: https://www.ebsco.com/products/research-databases/inspec-full-text providerName: EBSCOhost |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1LSwMxEA7aIt58VVRqCeg1rcnm0V4EUUsRFUEL9bTksYFiWxfbIvXiX3eyu65VELx4WFhIls1jhnyTmfkGoWPrZMS1jIiS2hPQREu0MI74tmfKMTC6RMh3vrmVvT6_GohBUao05MLAIKZNPUxziuBh2tIWnnwRSer8F-OAjFqBcRMEiLVbtKM6YOeBhR8F1BLTuGxrwlerqCoFYPUKqvZv784ec2pKThiPBhmhqqAw2KxOKgAaEeIdxScN0fJ_vsHQdTijcnc5vM8nqV686tFo6XTqbqD3cl5ZUMpTcz4zTfv2g_Lx_ya-iWpfmYP4rjwTt9BKMtlGa1mYqZ3uoNO8wDA04RCaisdZeQpcEslO8ewZ2wDoh36Bz0OSIKEdnIab7vHQ1lC_e_lw3iNFBQeSUgkq6JR0ljnruImU8CZxnlIPIFErx7Vri4QlSmvfppZTYzUHY5RSZ4URnCXCR7uoMnmeJHsIe8WlpV4FRyPXIE-GAZqymnmWaG5O9lH9c2viQg2nMeuIwNcPa7OPjsrtitOcyCPOHPAyikVcLBn0Kjfy914Hf-pVR5XZyzw5BMwyMw1UPbu4ub5vFAL4AYjN4mk |
| linkProvider | Unpaywall |
| linkToUnpaywall | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1LS8NAEF60Rbz5qlhRWdDrpu5mH81FELEUQenBQj2FfWShqDXYFNGLf93ZJMYqCF48BAK7IfuYYb_ZmfkGoRPrZMy1jImS2hPQREu0MI74vmfKMTC6RMh3vr6RwzG_mohJXao05MLAIOaRnuYVRfA072kLT7WIJHf-i3FAxr3AuAkCxPo9mqgE7Dyw8OOAWlKaNm0RfLWK2lIAVm-h9vhmdH5XUVNywng8KQlVBYXBlnVSAdCIEO8oPmmIlv_zDYauwxlVucvhfTHL9euLfnhYOp0GG-i9mVcZlHIfLQoT2bcflI__N_FN1PnKHMSj5kzcQivZbButlWGmdr6DzqoCw9CEQ2gqfizLU-CGSHaOiydsA6Cf-ld8EZIECU1wHm66H6e2g8aDy9uLIakrOJCcSlBBp6SzzFnHTayEN5nzlHoAiVo5rl1fZCxTWvs-tZwaqzkYo5Q6K4zgLBM-3kWt2dMs20PYKy4t9So4GrkGeTIM0JTVzLNMc3PaRQefW5PWajhPWSICXz-sTRcdN9uV5hWRR1o64GWcirReMujVbOTvvfb_1OsAtYrnRXYImKUwR7XgfQB9Z-DV |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=proceeding&rft.title=AIP+conference+proceedings&rft.atitle=Employing+data+mining+techniques+to+classify+Covid-19+pandemic&rft.date=2024-03-15&rft.pub=American+Institute+of+Physics&rft.issn=0094-243X&rft.eissn=1551-7616&rft.volume=3036&rft.issue=1&rft_id=info:doi/10.1063%2F5.0196328&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0094-243X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0094-243X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0094-243X&client=summon |