{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T09:20:40Z","timestamp":1771233640701,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,2,28]],"date-time":"2023-02-28T00:00:00Z","timestamp":1677542400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>COVID-19 has raised the issue of fighting epidemics. We were able to realize that in this fight, countering the spread of the disease was the main goal and we propose to contribute to it. To achieve this, we propose an enriched model of Random Forest (RF) that we called RF EP (EP for Epidemiological Prediction). RF is based on the Forest RI algorithm, proposed by Leo Breiman. Our model (RF EP) is based on a modified version of Forest RI that we called Forest EP. Operations added on Forest RI to obtain Forest EP are as follows: the selection of significant variables, the standardization of data, the reduction in dimensions, and finally the selection of new variables that best synthesize information the algorithm needs. This study uses a data set designed for classification studies to predict whether a patient is suffering from COVID-19 based on the following 11 variables: Country, Age, Fever, Bodypain, Runny_nose, Difficult_in_breathing, Nasal_congestion, Sore_throat, Gender, Severity, and Contact_with_covid_patient. We compared default RF to five other machine learning models: GNB, LR, SVM, KNN, and DT. RF proved to be the best classifier of all with the following metrics: Accuracy (94.9%), Precision (94.0%), Recall (96.6%), and F1 Score (95.2%). Our model, RF EP, produced the following metrics: Accuracy (94.9%), Precision (93.1%), Recall (97.7%), and F1 Score (95.3%). The performance gain by RF EP on the Recall metric compared to default RF allowed us to propose a new model with a better score than default RF in the limitation of the virus propagation on the dataset used in this study.<\/jats:p>","DOI":"10.3390\/computers12030054","type":"journal-article","created":{"date-parts":[[2023,2,28]],"date-time":"2023-02-28T03:00:38Z","timestamp":1677553238000},"page":"54","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Machine Learning Model for Predicting Epidemics"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4165-2702","authenticated-orcid":false,"given":"Patrick","family":"Loola Bokonda","sequence":"first","affiliation":[{"name":"SiWeb Team, Mohammadia School of Engineers (EMI), Mohammed V University in Rabat, Rabat 10000, Morocco"}]},{"given":"Moussa","family":"Sidibe","sequence":"additional","affiliation":[{"name":"Digital Sciences, Universit\u00e9 Paris Cit\u00e9, 75013 Paris, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5593-4674","authenticated-orcid":false,"given":"Nissrine","family":"Souissi","sequence":"additional","affiliation":[{"name":"Systems Engineering and Digital Transformation Laboratory-LISTD, Rabat National Higher School of Mines (ENSMR), Rabat 53000, Morocco"}]},{"given":"Khadija","family":"Ouazzani-Touhami","sequence":"additional","affiliation":[{"name":"Systems Engineering and Digital Transformation Laboratory-LISTD, Rabat National Higher School of Mines (ENSMR), Rabat 53000, Morocco"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"113661","DOI":"10.1016\/j.eswa.2020.113661","article-title":"A machine learning model to identify early stage symptoms of SARS-CoV-2 infected patients","volume":"160","author":"Ahamad","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1006\/clin.1997.4412","article-title":"Epidemiology and estimated population burden of selected autoimmune diseases in the United States","volume":"84","author":"Jacobson","year":"1997","journal-title":"Clin. Immunol. Immunopathol."},{"key":"ref_3","unstructured":"Ainsworth, M., and Over, A.M. (1997). Confronting AIDS: Public Priorities in a Global Epidemic, World Bank Group."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Birge, J.R., Candogan, O., and Feng, Y. (2020). Controlling Epidemic Spread: Reducing Economic Losses with Targeted Closures, University of Chicago. University of Chicago, Becker Friedman Institute for Economics Working Paper.","DOI":"10.2139\/ssrn.3590621"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Bokonda, P.L., Ouazzani-Touhami, K., and Souissi, N. (2020, January 25\u201327). Predictive analysis using machine learning: Review of trends and methods. Proceedings of the 2020 International Symposium on Advanced Electrical and Communication Technologies (ISAECT), Kenitra, Morocco.","DOI":"10.1109\/ISAECT50560.2020.9523703"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Bokonda, P.L., Ouazzani-Touhami, K., and Souissi, N. (2021, January 27\u201330). Which Machine Learning method for outbreaks predictions?. Proceedings of the 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA.","DOI":"10.1109\/CCWC51732.2021.9376061"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"101489","DOI":"10.1109\/ACCESS.2020.2997311","article-title":"COVID-19 future forecasting using supervised machine learning models","volume":"8","author":"Rustam","year":"2020","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"104807","DOI":"10.1016\/j.ijmedinf.2022.104807","article-title":"Outcome prediction during an ICU surge using a purely data-driven approach: A supervised machine learning case-study in critically ill patients from COVID-19 Lombardy outbreak","volume":"164","author":"Greco","year":"2022","journal-title":"Int. J. Med. Inform."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1007\/s42979-020-00216-w","article-title":"Predictive data mining models for novel coronavirus (COVID-19) infected patients\u2019 recovery","volume":"1","author":"Muhammad","year":"2020","journal-title":"SN Comput. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1207","DOI":"10.1007\/s10044-021-00984-y","article-title":"Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks","volume":"24","author":"Narin","year":"2021","journal-title":"Pattern Anal. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"103792","DOI":"10.1016\/j.compbiomed.2020.103792","article-title":"Automated detection of COVID-19 cases using deep neural networks with X-ray images","volume":"121","author":"Ozturk","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Mirri, S., Delnevo, G., and Roccetti, M. (2020). Is a COVID-19 second wave possible in Emilia-Romagna (Italy)? Forecasting a future outbreak with particulate pollution and machine learning. Computation, 8.","DOI":"10.3390\/computation8030074"},{"key":"ref_13","first-page":"622","article-title":"Prediction of the final size for COVID-19 epidemic using machine learning: A case study of Egypt","volume":"5","author":"Amar","year":"2020","journal-title":"Infect. Dis. Model."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"165","DOI":"10.21037\/jtd.2020.02.64","article-title":"Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions","volume":"12","author":"Yang","year":"2020","journal-title":"J. Thorac. Dis."},{"key":"ref_15","unstructured":"Dianbo, L., Leonardo, C., and Canelle, P. (2020). A machine learning methodology for real-time forecasting of the 2019\u20132020 COVID-19 outbreak using Internet searches, news alerts, and estimates from mechanistic models. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1225","DOI":"10.1016\/S0140-6736(20)30627-9","article-title":"COVID-19, and Italy: What next?","volume":"395","author":"Remuzzi","year":"2020","journal-title":"Lancet"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Petropoulos, F., and Makridakis, S. (2020). Forecasting the novel coronavirus COVID-19. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0231236"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1545","DOI":"10.1001\/jama.2020.4031","article-title":"Critical care utilization for the COVID-19 outbreak in Lombardy, Italy: Early experience and forecast during an emergency response","volume":"323","author":"Grasselli","year":"2020","journal-title":"JAMA"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/s42979-020-00394-7","article-title":"Supervised machine learning models for prediction of COVID-19 infection using epidemiology dataset","volume":"2","author":"Muhammad","year":"2021","journal-title":"SN Comput. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2479","DOI":"10.17576\/jsm-2021-5008-28","article-title":"Prediction of COVID-19 patient using supervised machine learning algorithm","volume":"50","author":"Buvana","year":"2021","journal-title":"Sains Malays."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Tapak, L., Hamidi, O., Fathian, M., and Karami, M. (2019). Comparative evaluation of time series models for predicting influenza outbreaks: Application of influenza-like illness data from sentinel sites of healthcare centers in Iran. BMC Res. Notes, 12.","DOI":"10.1186\/s13104-019-4393-y"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1111\/tbed.13424","article-title":"Prediction for global African swine fever outbreaks based on a combination of random forest algorithms and meteorological data","volume":"67","author":"Liang","year":"2020","journal-title":"Transbound. Emerg. Dis."},{"key":"ref_23","unstructured":"Ducharme, G.R. (2018). Quality criteria of a generalist classifier. arXiv."},{"key":"ref_24","unstructured":"Simran, P. (2022, November 12). n\/a COVID-19 Dataset. Available online: https:\/\/github.com\/Simranpandey16\/COVID-19-prediction\/blob\/master\/Madedata1.csv."},{"key":"ref_25","unstructured":"(2022, November 12). WHO COVID-19 Research Database. Available online: https:\/\/pesquisa.bvsalud.org\/global-literature-on-novel-coronavirus-2019-ncov\/resource\/pt\/covidwho-1399685?lang=en."},{"key":"ref_26","unstructured":"Simran, P. (2022, November 12). n\/a Profile. Available online: http:\/\/www.simranpandey.com\/."},{"key":"ref_27","unstructured":"Kaur, H., and Kumari, V. (2018). Predictive modelling and analytics for diabetes using a machine learning approach. Appl. Comput. Inform."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/j.neuroimage.2017.09.001","article-title":"Fast Gaussian Na\u00efve Bayes for searchlight classification analysis","volume":"163","author":"Valente","year":"2017","journal-title":"Neuroimage"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Asadi, H., Dowling, R., Yan, B., and Mitchell, P. (2014). Machine learning for outcome prediction of acute ischemic stroke post intra-arterial therapy. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0088225"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"e18828","DOI":"10.2196\/18828","article-title":"Predicting COVID-19 incidence through analysis of Google trends data in Iran: Data mining and deep learning pilot study","volume":"6","author":"Ayyoubzadeh","year":"2020","journal-title":"JMIR Public Health Surveil."},{"key":"ref_31","first-page":"78","article-title":"Data mining driven models for diagnosis of diabetes mellitus: A survey","volume":"11","author":"Ishaq","year":"2018","journal-title":"Indian J. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1565","DOI":"10.1038\/nbt1206-1565","article-title":"What is a support vector machine?","volume":"24","author":"Noble","year":"2006","journal-title":"Nat. Biotechnol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1002\/wics.49","article-title":"Support vector machines","volume":"1","author":"Mammone","year":"2009","journal-title":"Wiley Interdisciplinary Reviews: Computational Statistics"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1145\/234313.234346","article-title":"Learning decision tree classifiers","volume":"28","author":"Quinlan","year":"1996","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Suthaharan, S. (2016). Machine Learning Models and Algorithms for Big Data Classification, Springer.","DOI":"10.1007\/978-1-4899-7641-3"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/BF00116251","article-title":"Induction of Decision Trees","volume":"1","author":"Quinlan","year":"1986","journal-title":"Mach. Learn."},{"key":"ref_37","first-page":"3092","article-title":"Uncertain data decision tree classification","volume":"29","author":"Li","year":"2009","journal-title":"J. Comput. Appl."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1016\/0031-3203(90)90098-6","article-title":"Multiple binary decision tree classifiers","volume":"23","author":"Shlien","year":"1990","journal-title":"Pattern Recognit."},{"key":"ref_39","unstructured":"Ho, T. (1995, January 14\u201316). Random Decision Forest. In Proceeding of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC, Canada."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_41","unstructured":"Grandini, M., Bagli, E., and Visani, G. (2020). Metrics for multi-class classification: An overview. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1159\/000353863","article-title":"Diagnostic accuracy measures","volume":"36","author":"Eusebi","year":"2013","journal-title":"Cerebrovasc. Dis."},{"key":"ref_43","first-page":"1","article-title":"The truth of the F-measure","volume":"1","author":"Sasaki","year":"2007","journal-title":"Teach Tutor Mater"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Susmaga, R. (2004). Confusion Matrix Visualization, Springer.","DOI":"10.1007\/978-3-540-39985-8_12"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"4749","DOI":"10.35940\/ijitee.L3583.1081219","article-title":"Open data kit: Mobile data collection framework for developing countries","volume":"8","author":"Bokonda","year":"2019","journal-title":"Int. J. Innov. Technol. Explor. Eng. (IJITEE)"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Bernard, S., Adam, S., and Heutte, L. (2007, January 23\u201326). Using random forests for handwritten digit recognition. Proceedings of the Ninth International Conference on Document Analysis and Recognition (ICDAR 2007), Curitiba, Brazil.","DOI":"10.1109\/ICDAR.2007.4377074"},{"key":"ref_47","first-page":"543","article-title":"Mobile Data Collection Using Open Data Kit","volume":"Volume 3","author":"Souissi","year":"2019","journal-title":"Innovation in Information Systems and Technologies to Support Learning Research: Proceedings of EMENA-ISTL"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"19","DOI":"10.3991\/ijim.v14i13.13483","article-title":"A Practical Analysis of Mobile Data Collection Apps","volume":"14","author":"Bokonda","year":"2020","journal-title":"Int. J. Interact. Mob. Technol."}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/12\/3\/54\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:43:42Z","timestamp":1760121822000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/12\/3\/54"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,28]]},"references-count":48,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["computers12030054"],"URL":"https:\/\/doi.org\/10.3390\/computers12030054","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,28]]}}}