{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T23:37:25Z","timestamp":1773790645428,"version":"3.50.1"},"reference-count":78,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,4,15]],"date-time":"2021-04-15T00:00:00Z","timestamp":1618444800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>Tuberculosis (TB) is an airborne infectious disease caused by organisms in the Mycobacterium tuberculosis (Mtb) complex. In many low and middle-income countries, TB remains a major cause of morbidity and mortality. Once a patient has been diagnosed with TB, it is critical that healthcare workers make the most appropriate treatment decision given the individual conditions of the patient and the likely course of the disease based on medical experience. Depending on the prognosis, delayed or inappropriate treatment can result in unsatisfactory results including the exacerbation of clinical symptoms, poor quality of life, and increased risk of death. This work benchmarks machine learning models to aid TB prognosis using a Brazilian health database of confirmed cases and deaths related to TB in the State of Amazonas. The goal is to predict the probability of death by TB thus aiding the prognosis of TB and associated treatment decision making process. In its original form, the data set comprised 36,228 records and 130 fields but suffered from missing, incomplete, or incorrect data. Following data cleaning and preprocessing, a revised data set was generated comprising 24,015 records and 38 fields, including 22,876 reported cured TB patients and 1139 deaths by TB. To explore how the data imbalance impacts model performance, two controlled experiments were designed using (1) imbalanced and (2) balanced data sets. The best result is achieved by the Gradient Boosting (GB) model using the balanced data set to predict TB-mortality, and the ensemble model composed by the Random Forest (RF), GB and Multi-Layer Perceptron (MLP) models is the best model to predict the cure class.<\/jats:p>","DOI":"10.3390\/informatics8020027","type":"journal-article","created":{"date-parts":[[2021,4,15]],"date-time":"2021-04-15T21:35:13Z","timestamp":1618522513000},"page":"27","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Benchmarking Machine Learning Models to Assist in the Prognosis of Tuberculosis"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0275-3298","authenticated-orcid":false,"given":"Maicon Herverton","family":"Lino Ferreira da Silva Barros","sequence":"first","affiliation":[{"name":"Programa de P\u00f3s-Gradua\u00e7\u00e3o em Engenharia de Computa\u00e7\u00e3o (PPGEC), Universidade de Pernambuco, Recife 50720-001, Pernambuco, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2084-5516","authenticated-orcid":false,"given":"Geovanne","family":"Oliveira Alves","sequence":"additional","affiliation":[{"name":"Programa de P\u00f3s-Gradua\u00e7\u00e3o em Engenharia de Computa\u00e7\u00e3o (PPGEC), Universidade de Pernambuco, Recife 50720-001, Pernambuco, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2188-6272","authenticated-orcid":false,"given":"Lubnnia","family":"Morais Flor\u00eancio Souza","sequence":"additional","affiliation":[{"name":"Programa de P\u00f3s-Gradua\u00e7\u00e3o em Engenharia de Computa\u00e7\u00e3o (PPGEC), Universidade de Pernambuco, Recife 50720-001, Pernambuco, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7742-2995","authenticated-orcid":false,"given":"Elisson","family":"da Silva Rocha","sequence":"additional","affiliation":[{"name":"Programa de P\u00f3s-Gradua\u00e7\u00e3o em Engenharia de Computa\u00e7\u00e3o (PPGEC), Universidade de Pernambuco, Recife 50720-001, Pernambuco, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1150-4904","authenticated-orcid":false,"given":"Jo\u00e3o Fausto","family":"Lorenzato de Oliveira","sequence":"additional","affiliation":[{"name":"Programa de P\u00f3s-Gradua\u00e7\u00e3o em Engenharia de Computa\u00e7\u00e3o (PPGEC), Universidade de Pernambuco, Recife 50720-001, Pernambuco, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9284-7580","authenticated-orcid":false,"given":"Theo","family":"Lynn","sequence":"additional","affiliation":[{"name":"Business School, Dublin City University, Dublin 9, Dublin, Ireland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7307-8851","authenticated-orcid":false,"given":"Vanderson","family":"Sampaio","sequence":"additional","affiliation":[{"name":"Funda\u00e7\u00e3o de Medicina Tropical Doutor Heitor Vieira Dourado, Manaus 69040-000, Amazonas, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9163-5583","authenticated-orcid":false,"given":"Patricia Takako","family":"Endo","sequence":"additional","affiliation":[{"name":"Programa de P\u00f3s-Gradua\u00e7\u00e3o em Engenharia de Computa\u00e7\u00e3o (PPGEC), Universidade de Pernambuco, Recife 50720-001, Pernambuco, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"16076","DOI":"10.1038\/nrdp.2016.76","article-title":"Tuberculosis","volume":"2","author":"Pai","year":"2016","journal-title":"Nat. Rev. Dis. Prim."},{"key":"ref_2","unstructured":"WHO (2021, January 25). Global Tuberculosis Report 2020. Available online: https:\/\/apps.who.int\/iris\/bitstream\/handle\/10665\/336069\/9789240013131-eng.pdf."},{"key":"ref_3","unstructured":"(2020, September 25). Tuberculosis Profile: Brazil. Available online: https:\/\/worldhealthorg.shinyapps.io\/tb_profiles?_inputs_&lan=%22EN%22&iso2=%22BR%22."},{"key":"ref_4","unstructured":"WHO (2020, September 29). Country Profiles for 30 High TB Burden Countries. Available online: https:\/\/www.who.int\/tb\/publications\/global_report\/tb19_Report_country_profiles_15October2019.pdf?ua=1."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ranzani, O.T., Pescarini, J.M., Martinez, L., and Garcia-Basteiro, A.L. (2021). Increasing tuberculosis burden in Latin America: An alarming trend for global control efforts. BMJ.","DOI":"10.1136\/bmjgh-2021-005639"},{"key":"ref_6","unstructured":"(2021, January 25). Sistema \u00danico de Sa\u00fade (SUS): Estrutura, Princ\u00edpios e Como Funciona, Available online: https:\/\/antigo.saude.gov.br\/sistema-unico-de-saude."},{"key":"ref_7","unstructured":"(2021, January 28). Brasil \u00e9 \u00fanico com \u2018SUS\u2019 Entre Pa\u00edses Com Mais de 200 Milh\u00f5es de Habitantes. Available online: https:\/\/www1.folha.uol.com.br\/cotidiano\/2019\/10\/brasil-e-unico-com-sus-entre-paises-com-mais-de-200-milhoes-de-habitantes.shtml."},{"key":"ref_8","unstructured":"(2021, January 25). Brazil\u2019s Sistema \u00danico da Sa\u00fade (SUS): Caught in the Cross Fire. Available online: https:\/\/www.csis.org\/blogs\/smart-global-health\/brazils-sistema-unico-da-saude-sus-caught-cross-fire."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1229","DOI":"10.1016\/j.jclinepi.2006.02.005","article-title":"Prognosis research: Why is Dr. Lydgate still waiting?","volume":"59","author":"Hemingway","year":"2006","journal-title":"J. Clin. Epidemiol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"b4184","DOI":"10.1136\/bmj.b4184","article-title":"Ten steps towards improving prognosis research","volume":"339","author":"Hemingway","year":"2009","journal-title":"BMJ"},{"key":"ref_11","first-page":"2319","article-title":"A Review of Ensemble Based Classification and Clustering in Machine Learning","volume":"12","author":"Bora","year":"2019","journal-title":"Int. J. New Innov. Eng. Technol."},{"key":"ref_12","unstructured":"Garc\u00eda-Gil, D., Holmberg, J., Garc\u00eda, S., Xiong, N., and Herrera, F. (2020). Smart Data based Ensemble for Imbalanced Big Data Classification. arXiv."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1387","DOI":"10.1109\/TNNLS.2019.2920246","article-title":"Hybrid Classifier Ensemble for Imbalanced Data","volume":"31","author":"Yang","year":"2019","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_14","first-page":"1","article-title":"Diagn\u00f3stico e Tratamento Medicamentoso Em Casos de Tuberculose Pulmonar: Revis\u00e3o de Literatura","volume":"7","author":"Martins","year":"2020","journal-title":"Rev. Sa\u00fade Multidiscip."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1148\/radiol.2017162326","article-title":"Deep learning at chest radiography: Automated classification of pulmonary tuberculosis by using convolutional neural networks","volume":"284","author":"Lakhani","year":"2017","journal-title":"Radiology"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Rajaraman, S., Candemir, S., Xue, Z., Alderson, P.O., Kohli, M., Abuya, J., Thoma, G.R., and Antani, S. (2018, January 18\u201321). A novel stacked generalization of models for improved TB detection in chest radiographs. Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA.","DOI":"10.1109\/EMBC.2018.8512337"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Hooda, R., Sofat, S., Kaur, S., Mittal, A., and Meriaudeau, F. (2017, January 12\u201314). Deep-learning: A potential method for tuberculosis detection using chest radiography. Proceedings of the 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), Kuching, Malaysia.","DOI":"10.1109\/ICSIPA.2017.8120663"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Sethi, K., Parmar, V., and Suri, M. (2018, January 17\u201319). Low-Power Hardware-Based Deep-Learning Diagnostics Support Case Study. Proceedings of the 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS), Cleveland, OH, USA.","DOI":"10.1109\/BIOCAS.2018.8584697"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kant, S., and Srivastava, M.M. (2018, January 18\u201321). Towards automated tuberculosis detection using deep learning. Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence (SSCI), Bangalore, India.","DOI":"10.1109\/SSCI.2018.8628800"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Carneiro, G., Oakden-Rayner, L., Bradley, A.P., Nascimento, J., and Palmer, L. (2017, January 18\u201321). Automated 5-year mortality prediction using deep learning and radiomics features from chest computed tomography. Proceedings of the 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), Melbourne, VIC, Australia.","DOI":"10.1109\/ISBI.2017.7950485"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.neucom.2016.11.018","article-title":"An evolutionary deep neural network for predicting morbidity of gastrointestinal infections by food contamination","volume":"226","author":"Song","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_22","first-page":"649","article-title":"Development and validation of a deep neural network model for prediction of postoperative in-hospital mortality","volume":"129","author":"Lee","year":"2018","journal-title":"Anesthesiol. J. Am. Soc. Anesthesiol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"e044687","DOI":"10.1136\/bmjopen-2020-044687","article-title":"Systematic review of prediction models for pulmonary tuberculosis treatment outcomes in adults","volume":"11","author":"Peetluk","year":"2021","journal-title":"BMJ Open"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3020","DOI":"10.1017\/S0950268817001911","article-title":"Predicting treatment failure, death and drug resistance using a computed risk score among newly diagnosed TB patients in Tamaulipas, Mexico","volume":"145","author":"Abdelbary","year":"2017","journal-title":"Epidemiol. Infect."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"267","DOI":"10.15537\/smj.2018.3.22280","article-title":"Mortality of patients hospitalized for active tuberculosis in King Abdulaziz University Hospital, Jeddah, Saudi Arabia","volume":"39","author":"Aljohaney","year":"2018","journal-title":"Saudi Med. J."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Bastos, H.N., Os\u00f3rio, N.S., Castro, A.G., Ramos, A., Carvalho, T., Meira, L., Ara\u00fajo, D., Almeida, L., Boaventura, R., and Fragata, P. (2016). A prediction rule to stratify mortality risk of patients with pulmonary tuberculosis. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0162797"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Gupta-Wright, A., Corbett, E.L., Wilson, D., van Oosterhout, J.J., Dheda, K., Huerga, H., Peter, J., Bonnet, M., Alufandika-Moyo, M., and Grint, D. (2019). Risk score for predicting mortality including urine lipoarabinomannan detection in hospital inpatients with HIV-associated tuberculosis in sub-Saharan Africa: Derivation and external validation cohort study. PLoS Med., 16.","DOI":"10.1371\/journal.pmed.1002776"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"54","DOI":"10.5588\/ijtld.12.0476","article-title":"Development and validation of a tuberculosis prognostic score for smear-positive in-patients in Japan","volume":"17","author":"Horita","year":"2013","journal-title":"Int. J. Tuberc. Lung Dis."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"389","DOI":"10.7196\/SAMJ.9148","article-title":"Validation of a severity-of-illness score in patients with tuberculosis requiring intensive care unit admission","volume":"105","author":"Koegelenberg","year":"2015","journal-title":"S. Afr. Med. J."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/j.jinf.2018.02.009","article-title":"Development and validation of a prognostic score to predict tuberculosis mortality","volume":"77","author":"Nguyen","year":"2018","journal-title":"J. Infect."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12879-018-3632-5","article-title":"Development and validation of a risk score to predict mortality during TB treatment in patients with TB-diabetes comorbidity","volume":"19","author":"Nguyen","year":"2019","journal-title":"BMC Infect. Dis."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Nguyen, D.T., Jenkins, H.E., and Graviss, E.A. (2018). Prognostic score to predict mortality during TB treatment in TB\/HIV co-infected patients. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0196022"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Pefura-Yone, E.W., Balkissou, A.D., Poka-Mayap, V., Fatime-Abaicho, H.K., Enono-Edende, P.T., and Kengne, A.P. (2017). Development and validation of a prognostic score during tuberculosis treatment. BMC Infect. Dis., 17.","DOI":"10.1186\/s12879-017-2309-9"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"198","DOI":"10.5588\/ijtld.12.0224","article-title":"Health care index score and risk of death following tuberculosis diagnosis in HIV-positive patients","volume":"17","author":"Podlekareva","year":"2013","journal-title":"Int. J. Tuberc. Lung Dis."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1155\/2012\/361292","article-title":"Tuberculosis in the intensive care unit: A retrospective descriptive cohort study with determination of a predictive fatality score","volume":"23","author":"Valade","year":"2012","journal-title":"Can. J. Infect. Dis. Med. Microbiol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12931-019-1004-3","article-title":"Prognostic value of serum macrophage migration inhibitory factor levels in pulmonary tuberculosis","volume":"20","author":"Wang","year":"2019","journal-title":"Respir. Res."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1080\/00365540701558698","article-title":"TBscore: Signs and symptoms from tuberculosis patients in a low-resource setting have predictive value and may be used to assess clinical course","volume":"40","author":"Wejse","year":"2008","journal-title":"Scand. J. Infect. Dis."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"136","DOI":"10.5582\/bst.2018.01309","article-title":"A Clinical scoring model to predict mortality in HIV\/TB co-infected patients at end stage of AIDS in China: An observational cohort study","volume":"13","author":"Zhang","year":"2019","journal-title":"Biosci. Trends"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1080\/17538157.2018.1433676","article-title":"Predicting treatment outcome of drug-susceptible tuberculosis patients using machine-learning models","volume":"44","author":"Hussain","year":"2019","journal-title":"Inform. Health Soc. Care"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Killian, J.A., Wilder, B., Sharma, A., Choudhary, V., Dilkina, B., and Tambe, M. (2019, January 4\u20138). Learning to prescribe interventions for tuberculosis patients using digital adherence data. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA.","DOI":"10.1145\/3292500.3330777"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Sauer, C.M., Sasson, D., Paik, K.E., McCague, N., Celi, L.A., Sanchez Fernandez, I., and Illigens, B.M. (2018). Feature selection and prediction of treatment failure in tuberculosis. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0207491"},{"key":"ref_42","first-page":"10","article-title":"Evaluation and comparison of different machine learning methods to predict outcome of tuberculosis treatment course","volume":"5","author":"Kalhori","year":"2013","journal-title":"J. Intell. Learn. Syst. Appl."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Kira, K., and Rendell, L.A. (1992). A practical approach to feature selection. Machine Learning Proceedings 1992, Elsevier.","DOI":"10.1016\/B978-1-55860-247-2.50037-1"},{"key":"ref_44","unstructured":"Rocha, E.D.S. (2020). DEEPTUB: Plataforma Para Predi\u00e7\u00e3O De Morte Por Tuberculose Baseado Em Modelos De Deep Learning Utilizando Dados Demogr\u00e1Ficos, Cl\u00edNicos E Laboratoriais. [Disserta\u00e7\u00e3o de Mestrado, Universidade de Pernambuco]."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Marcano-Cedeno, A., Quintanilla-Dom\u00ednguez, J., Cortina-Januchs, M., and Andina, D. (2010, January 7\u201310). Feature selection using sequential forward selection and classification applying artificial metaplasticity neural network. Proceedings of the IECON 2010\u201436th Annual Conference on IEEE Industrial Electronics Society, Glendale, AZ, USA.","DOI":"10.1109\/IECON.2010.5675075"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.16984\/saufenbilder.501799","article-title":"Feature selection with sequential forward selection algorithm from emotion estimation based on EEG signals","volume":"23","year":"2019","journal-title":"Sak. \u00dcniversitesi Fen Bilim. Enstit\u00fcs\u00fc derg."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1007\/s10772-016-9358-0","article-title":"An optimal two stage feature selection for speech emotion recognition using acoustic features","volume":"19","author":"Kuchibhotla","year":"2016","journal-title":"Int. J. Speech Technol."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Varma, M., and Jereesh, A. (2017, January 20\u201321). Identifying predominant clinical and genomic features for glioblastoma multiforme using sequential backward selection. Proceedings of the 2017 International Conference on Circuit, Power and Computing Technologies (ICCPCT), Kollam, India.","DOI":"10.1109\/ICCPCT.2017.8074297"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Lingampeta, D., and Yalamanchili, B. (2020, January 26\u201328). Human Emotion Recognition using Acoustic Features with Optimized Feature Selection and Fusion Techniques. Proceedings of the 2020 International Conference on Inventive Computation Technologies (ICICT), Coimbatore, India.","DOI":"10.1109\/ICICT48043.2020.9112452"},{"key":"ref_50","first-page":"1301","article-title":"A survey on machine learning: Concept, algorithms and applications","volume":"5","author":"Das","year":"2017","journal-title":"Int. J. Innov. Res. Comput. Commun. Eng."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Callahan, A., and Shah, N.H. (2017). Machine learning in healthcare. Key Advances in Clinical Informatics, Elsevier.","DOI":"10.1016\/B978-0-12-809523-2.00019-4"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Bonte, C., and Vercauteren, F. (2018). Privacy-preserving logistic regression training. BMC Med. Genom., 11.","DOI":"10.1186\/s12920-018-0398-y"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Menard, S. (2002). Applied Logistic Regression Analysis, SAGE.","DOI":"10.4135\/9781412983433"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1111\/j.1469-1809.1936.tb02137.x","article-title":"The use of multiple measurements in taxonomic problems","volume":"7","author":"Fisher","year":"1936","journal-title":"Ann. Eugen."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Xanthopoulos, P., Pardalos, P.M., and Trafalis, T.B. (2013). Linear discriminant analysis. Robust Data Mining, Springer.","DOI":"10.1007\/978-1-4419-9878-1"},{"key":"ref_56","first-page":"1","article-title":"Linear discriminant analysis-a brief tutorial","volume":"18","author":"Balakrishnama","year":"1998","journal-title":"Inst. Signal Inf. Process."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Basha, S.M., and Rajput, D.S. (2019). Survey on Evaluating the Performance of Machine Learning Algorithms: Past Contributions and Future Roadmap. Deep Learning and Parallel Computing Environment for Bioengineering Systems, Elsevier.","DOI":"10.1016\/B978-0-12-816718-2.00016-6"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Guo, G., Wang, H., Bell, D., Bi, Y., and Greer, K. (2003). KNN model-based approach in classification. OTM Confederated International Conferences \u201cOn the Move to Meaningful Internet Systems\u201d, Springer.","DOI":"10.1007\/978-3-540-39964-3_62"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"012021","DOI":"10.1088\/1742-6596\/1752\/1\/012021","article-title":"Na\u00efve Bayes Classifier and Particle Swarm Optimization Feature Selection Method for Classifying Intrusion Detection System Dataset","volume":"1752","author":"Talita","year":"2021","journal-title":"J. Phys. Conf. Ser. IOP Publ."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"012045","DOI":"10.1088\/1742-6596\/1752\/1\/012045","article-title":"Cerebral Infarction Classification Using the K-Nearest Neighbor and Naive Bayes Classifier","volume":"1752","author":"Rukmawan","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_61","unstructured":"Rish, I. (2001, January 4\u20136). An empirical study of the naive Bayes classifier. Proceedings of the IJCAI 2001 Workshop On Empirical Methods in Artificial Intelligence, Seattle, WA, USA."},{"key":"ref_62","unstructured":"da Silva, L.A., Peres, S.M., and Boscarioli, C. (2017). Introdu\u00e7\u00e3o \u00e0 Minera\u00e7\u00e3o de Dados: Com Aplica\u00e7\u00f5es em R, Elsevier."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.mechmachtheory.2013.10.006","article-title":"Optimum multi-fault classification of gears with integration of evolutionary and SVM algorithms","volume":"73","author":"Bordoloi","year":"2014","journal-title":"Mech. Mach. Theory"},{"key":"ref_64","first-page":"2632","article-title":"K-SVM: An Effective SVM Algorithm Based on K-means Clustering","volume":"8","author":"Yao","year":"2013","journal-title":"JCP"},{"key":"ref_65","unstructured":"Lu, H., Karimireddy, S.P., Ponomareva, N., and Mirrokni, V. (2020, January 3\u20135). Accelerating Gradient Boosting Machines. Proceedings of the International Conference on Artificial Intelligence and Statistics, PMLR, Palermo, Italy."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"21","DOI":"10.3389\/fnbot.2013.00021","article-title":"Gradient boosting machines, a tutorial","volume":"7","author":"Natekin","year":"2013","journal-title":"Front. Neurorobot."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1469","DOI":"10.1007\/s10994-017-5642-8","article-title":"Adaptive random forests for evolving data stream classification","volume":"106","author":"Gomes","year":"2017","journal-title":"Mach. Learn."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.eij.2012.08.002","article-title":"Support vector machines (SVMs) versus multilayer perception (MLP) in data classification","volume":"13","author":"Zanaty","year":"2012","journal-title":"Egypt. Inform. J."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Zhou, Z.H. (2012). Ensemble Methods: Foundations and Algorithms, CRC Press.","DOI":"10.1201\/b12207"},{"key":"ref_70","unstructured":"(2021, January 25). Dicion\u00e1rio de Dados-SINAN NET-Vers\u00e3o 5.0, Available online: http:\/\/portalsinan.saude.gov.br\/images\/documentos\/Agravos\/Tuberculose\/DICI_DADOS_NET_Tuberculose_23_07_2020.pdf."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Bad\u017ea, M.M., and Barjaktarovi\u0107, M.\u010c. (2020). Classification of brain tumors from MRI images using a convolutional neural network. Appl. Sci., 10.","DOI":"10.3390\/app10061999"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1157","DOI":"10.1213\/ANE.0000000000004539","article-title":"Prediction of an acute hypotensive episode during an ICU hospitalization with a super learner machine-learning algorithm","volume":"130","author":"Cherifa","year":"2020","journal-title":"Anesth. Analg."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"e15965","DOI":"10.2196\/15965","article-title":"A Predictive Model Based on Machine Learning for the Early Detection of Late-Onset Neonatal Sepsis: Development and Observational Study","volume":"8","author":"Song","year":"2020","journal-title":"JMIR Med. Inform."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"103540","DOI":"10.1016\/j.jbi.2020.103540","article-title":"Predictive modeling of bacterial infections and antibiotic therapy needs in critically ill adults","volume":"109","author":"Eickelberg","year":"2020","journal-title":"J. Biomed. Inform."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Ho Thanh Lam, L., Le, N.H., Van Tuan, L., Tran Ban, H., Nguyen Khanh Hung, T., Nguyen, N.T.K., Huu Dang, L., and Le, N.Q.K. (2020). Machine learning model for identifying antioxidant proteins using features calculated from primary sequences. Biology, 9.","DOI":"10.3390\/biology9100325"},{"key":"ref_76","unstructured":"Liashchynskyi, P., and Liashchynskyi, P. (2019). Grid Search, Random Search, Genetic Algorithm: A Big Comparison for NAS. arXiv."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Woolson, R. (2007). Wilcoxon signed-rank test. Wiley Encyclopedia of Clinical Trials, John Wiley & Sons, Inc.","DOI":"10.1002\/9780471462422.eoct979"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Le, N.Q.K., Do, D.T., Hung, T.N.K., Lam, L.H.T., Huynh, T.T., and Nguyen, N.T.K. (2020). A computational framework based on ensemble deep neural networks for essential genes identification. Int. J. Mol. Sci., 21.","DOI":"10.3390\/ijms21239070"}],"container-title":["Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2227-9709\/8\/2\/27\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:48:23Z","timestamp":1760161703000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2227-9709\/8\/2\/27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,15]]},"references-count":78,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2021,6]]}},"alternative-id":["informatics8020027"],"URL":"https:\/\/doi.org\/10.3390\/informatics8020027","relation":{"has-preprint":[{"id-type":"doi","id":"10.20944\/preprints202103.0284.v2","asserted-by":"object"},{"id-type":"doi","id":"10.20944\/preprints202103.0284.v1","asserted-by":"object"}]},"ISSN":["2227-9709"],"issn-type":[{"value":"2227-9709","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,15]]}}}