{"id":"https://openalex.org/W4322629045","doi":"https://doi.org/10.3390/computers12030054","title":"Machine Learning Model for Predicting Epidemics","display_name":"Machine Learning Model for Predicting Epidemics","publication_year":2023,"publication_date":"2023-02-28","ids":{"openalex":"https://openalex.org/W4322629045","doi":"https://doi.org/10.3390/computers12030054"},"language":"en","primary_location":{"id":"doi:10.3390/computers12030054","is_oa":true,"landing_page_url":"https://doi.org/10.3390/computers12030054","pdf_url":"https://www.mdpi.com/2073-431X/12/3/54/pdf?version=1677563480","source":{"id":"https://openalex.org/S4210228075","display_name":"Computers","issn_l":"2073-431X","issn":["2073-431X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2073-431X/12/3/54/pdf?version=1677563480","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5007156956","display_name":"Patrick Loola Bokonda","orcid":"https://orcid.org/0000-0003-4165-2702"},"institutions":[{"id":"https://openalex.org/I126477371","display_name":"Mohammed V University","ror":"https://ror.org/00r8w8f84","country_code":"MA","type":"education","lineage":["https://openalex.org/I126477371"]},{"id":"https://openalex.org/I127336678","display_name":"Ecole Mohammadia d'Ing\u00e9nieurs","ror":"https://ror.org/00md3qm14","country_code":"MA","type":"education","lineage":["https://openalex.org/I127336678"]}],"countries":["MA"],"is_corresponding":true,"raw_author_name":"Patrick Loola Bokonda","raw_affiliation_strings":["SiWeb Team, Mohammadia School of Engineers (EMI), Mohammed V University in Rabat, Rabat 10000, Morocco"],"raw_orcid":"https://orcid.org/0000-0003-4165-2702","affiliations":[{"raw_affiliation_string":"SiWeb Team, Mohammadia School of Engineers (EMI), Mohammed V University in Rabat, Rabat 10000, Morocco","institution_ids":["https://openalex.org/I127336678","https://openalex.org/I126477371"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009216546","display_name":"Moussa Sidib\u00e9","orcid":"https://orcid.org/0000-0001-5460-9557"},"institutions":[{"id":"https://openalex.org/I204730241","display_name":"Universit\u00e9 Paris Cit\u00e9","ror":"https://ror.org/05f82e368","country_code":"FR","type":"education","lineage":["https://openalex.org/I204730241"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Moussa Sidibe","raw_affiliation_strings":["Digital Sciences, Universit\u00e9 Paris Cit\u00e9, 75013 Paris, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Digital Sciences, Universit\u00e9 Paris Cit\u00e9, 75013 Paris, France","institution_ids":["https://openalex.org/I204730241"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044263659","display_name":"Nissrine Souissi","orcid":"https://orcid.org/0000-0002-5593-4674"},"institutions":[{"id":"https://openalex.org/I4210155111","display_name":"National School of Mineral Industry","ror":"https://ror.org/04a41pr43","country_code":"MA","type":"education","lineage":["https://openalex.org/I4210155111"]}],"countries":["MA"],"is_corresponding":false,"raw_author_name":"Nissrine Souissi","raw_affiliation_strings":["Systems Engineering and Digital Transformation Laboratory-LISTD, Rabat National Higher School of Mines (ENSMR), Rabat 53000, Morocco"],"raw_orcid":"https://orcid.org/0000-0002-5593-4674","affiliations":[{"raw_affiliation_string":"Systems Engineering and Digital Transformation Laboratory-LISTD, Rabat National Higher School of Mines (ENSMR), Rabat 53000, Morocco","institution_ids":["https://openalex.org/I4210155111"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080603164","display_name":"Khadija Ouazzani-Touhami","orcid":null},"institutions":[{"id":"https://openalex.org/I4210155111","display_name":"National School of Mineral Industry","ror":"https://ror.org/04a41pr43","country_code":"MA","type":"education","lineage":["https://openalex.org/I4210155111"]}],"countries":["MA"],"is_corresponding":false,"raw_author_name":"Khadija Ouazzani-Touhami","raw_affiliation_strings":["Systems Engineering and Digital Transformation Laboratory-LISTD, Rabat National Higher School of Mines (ENSMR), Rabat 53000, Morocco"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Systems Engineering and Digital Transformation Laboratory-LISTD, Rabat National Higher School of Mines (ENSMR), Rabat 53000, Morocco","institution_ids":["https://openalex.org/I4210155111"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5007156956"],"corresponding_institution_ids":["https://openalex.org/I126477371","https://openalex.org/I127336678"],"apc_list":{"value":1600,"currency":"CHF","value_usd":1732},"apc_paid":{"value":1600,"currency":"CHF","value_usd":1732},"fwci":1.1382,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.77769855,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"12","issue":"3","first_page":"54","last_page":"54"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9972000122070312,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9911999702453613,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.9185428023338318},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.586885929107666},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5827656388282776},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.5585299134254456},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5415083169937134},{"id":"https://openalex.org/keywords/recall","display_name":"Recall","score":0.5159843564033508},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5154853463172913},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.5109476447105408},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.49855756759643555},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.434933066368103},{"id":"https://openalex.org/keywords/f1-score","display_name":"F1 score","score":0.4298432469367981},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.3333936333656311},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.32120129466056824},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.23144406080245972},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.12930098176002502},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.1216372549533844}],"concepts":[{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.9185428023338318},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.586885929107666},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5827656388282776},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.5585299134254456},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5415083169937134},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.5159843564033508},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5154853463172913},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.5109476447105408},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.49855756759643555},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.434933066368103},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.4298432469367981},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.3333936333656311},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.32120129466056824},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.23144406080245972},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.12930098176002502},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.1216372549533844},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/computers12030054","is_oa":true,"landing_page_url":"https://doi.org/10.3390/computers12030054","pdf_url":"https://www.mdpi.com/2073-431X/12/3/54/pdf?version=1677563480","source":{"id":"https://openalex.org/S4210228075","display_name":"Computers","issn_l":"2073-431X","issn":["2073-431X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:41e76fa8be9a402b94dd531082116490","is_oa":true,"landing_page_url":"https://doaj.org/article/41e76fa8be9a402b94dd531082116490","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Computers, Vol 12, Iss 3, p 54 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/computers12030054","is_oa":true,"landing_page_url":"https://doi.org/10.3390/computers12030054","pdf_url":"https://www.mdpi.com/2073-431X/12/3/54/pdf?version=1677563480","source":{"id":"https://openalex.org/S4210228075","display_name":"Computers","issn_l":"2073-431X","issn":["2073-431X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Good health and well-being","score":0.8700000047683716,"id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4322629045.pdf"},"referenced_works_count":40,"referenced_works":["https://openalex.org/W1487097365","https://openalex.org/W1964940342","https://openalex.org/W1968452917","https://openalex.org/W2003233718","https://openalex.org/W2007471231","https://openalex.org/W2012908927","https://openalex.org/W2016193783","https://openalex.org/W2024502389","https://openalex.org/W2074617841","https://openalex.org/W2499581503","https://openalex.org/W2750651073","https://openalex.org/W2901779613","https://openalex.org/W2906126865","https://openalex.org/W2911964244","https://openalex.org/W2952382660","https://openalex.org/W2986202533","https://openalex.org/W2992853059","https://openalex.org/W3009876049","https://openalex.org/W3011242477","https://openalex.org/W3011508296","https://openalex.org/W3013056994","https://openalex.org/W3013360115","https://openalex.org/W3013507463","https://openalex.org/W3017855299","https://openalex.org/W3022589401","https://openalex.org/W3030419021","https://openalex.org/W3036259678","https://openalex.org/W3036674813","https://openalex.org/W3049085807","https://openalex.org/W3080977802","https://openalex.org/W3081347410","https://openalex.org/W3106763172","https://openalex.org/W3139209930","https://openalex.org/W3197578637","https://openalex.org/W3198175830","https://openalex.org/W4236137412","https://openalex.org/W4247897694","https://openalex.org/W4281785387","https://openalex.org/W6756270815","https://openalex.org/W6782325932"],"related_works":["https://openalex.org/W2985924212","https://openalex.org/W4327511089","https://openalex.org/W3034132578","https://openalex.org/W3195168932","https://openalex.org/W4293525103","https://openalex.org/W4288767684","https://openalex.org/W4220933319","https://openalex.org/W4221021152","https://openalex.org/W4383535405","https://openalex.org/W3188307501"],"abstract_inverted_index":{"COVID-19":[0,142],"has":[1],"raised":[2],"the":[3,18,21,24,59,97,102,106,112,121,145,181,187,205,225,247,250,254],"issue":[4],"of":[5,20,42,77,99,104,114,184,249],"fighting":[6],"epidemics.":[7],"We":[8,161],"were":[9],"able":[10],"to":[11,30,32,90,134,165,179,229,234],"realize":[12],"that":[13,46,80,117],"in":[14,108,246,257],"this":[15,258],"fight,":[16],"countering":[17],"spread":[19],"disease":[22],"was":[23],"main":[25],"goal":[26],"and":[27,110,159,175,196,214],"we":[28,37,47,81],"propose":[29,38,235],"contribute":[31],"it.":[33],"To":[34],"achieve":[35],"this,":[36],"an":[39],"enriched":[40],"model":[41,68,238],"Random":[43],"Forest":[44,60,78,83,88,92],"(RF)":[45],"called":[48,82],"RF":[49,55,164,177,202,222,231,245],"EP":[50,93,223],"(EP":[51],"for":[52,131],"Epidemiological":[53],"Prediction).":[54],"is":[56,71,139],"based":[57,72,143],"on":[58,73,87,144,224,253],"RI":[61,79,89],"algorithm,":[62],"proposed":[63],"by":[64,221],"Leo":[65],"Breiman.":[66],"Our":[67,200],"(RF":[69],"EP)":[70],"a":[74,127,137,236,240],"modified":[75],"version":[76],"EP.":[84],"Operations":[85],"added":[86],"obtain":[91],"are":[94],"as":[95],"follows:":[96],"selection":[98,113],"significant":[100],"variables,":[101],"standardization":[103],"data,":[105],"reduction":[107],"dimensions,":[109],"finally":[111],"new":[115,237],"variables":[116],"best":[118,182],"synthesize":[119],"information":[120],"algorithm":[122],"needs.":[123],"This":[124],"study":[125],"uses":[126],"data":[128],"set":[129],"designed":[130],"classification":[132],"studies":[133],"predict":[135],"whether":[136],"patient":[138],"suffering":[140],"from":[141],"following":[146,188,206],"11":[147],"variables:":[148],"Country,":[149],"Age,":[150],"Fever,":[151],"Bodypain,":[152],"Runny_nose,":[153],"Difficult_in_breathing,":[154],"Nasal_congestion,":[155],"Sore_throat,":[156],"Gender,":[157],"Severity,":[158],"Contact_with_covid_patient.":[160],"compared":[162,228],"default":[163,230,244],"five":[166],"other":[167],"machine":[168],"learning":[169],"models:":[170],"GNB,":[171],"LR,":[172],"SVM,":[173],"KNN,":[174],"DT.":[176],"proved":[178],"be":[180],"classifier":[183],"all":[185],"with":[186,239],"metrics:":[189,207],"Accuracy":[190,208],"(94.9%),":[191,209],"Precision":[192,210],"(94.0%),":[193],"Recall":[194,212,226],"(96.6%),":[195],"F1":[197,215],"Score":[198,216],"(95.2%).":[199],"model,":[201],"EP,":[203],"produced":[204],"(93.1%),":[211],"(97.7%),":[213],"(95.3%).":[217],"The":[218],"performance":[219],"gain":[220],"metric":[227],"allowed":[232],"us":[233],"better":[241],"score":[242],"than":[243],"limitation":[248],"virus":[251],"propagation":[252],"dataset":[255],"used":[256],"study.":[259]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}