Utilization of machine learning for dengue case screening.
Arboviruses
Artificial intelligence
Clinical signs
Healthcare systems
Journal
BMC public health
ISSN: 1471-2458
Titre abrégé: BMC Public Health
Pays: England
ID NLM: 100968562
Informations de publication
Date de publication:
11 Jun 2024
11 Jun 2024
Historique:
received:
28
11
2023
accepted:
07
06
2024
medline:
12
6
2024
pubmed:
12
6
2024
entrez:
11
6
2024
Statut:
epublish
Résumé
Dengue causes approximately 10.000 deaths and 100 million symptomatic infections annually worldwide, making it a significant public health concern. To address this, artificial intelligence tools like machine learning can play a crucial role in developing more effective strategies for control, diagnosis, and treatment. This study identifies relevant variables for the screening of dengue cases through machine learning models and evaluates the accuracy of the models. Data from reported dengue cases in the states of Rio de Janeiro and Minas Gerais for the years 2016 and 2019 were obtained through the National Notifiable Diseases Surveillance System (SINAN). The mutual information technique was used to assess which variables were most related to laboratory-confirmed dengue cases. Next, a random selection of 10,000 confirmed cases and 10,000 discarded cases was performed, and the dataset was divided into training (70%) and testing (30%). Machine learning models were then tested to classify the cases. It was found that the logistic regression model with 10 variables (gender, age, fever, myalgia, headache, vomiting, nausea, back pain, rash, retro-orbital pain) and the Decision Tree and Multilayer Perceptron (MLP) models achieved the best results in decision metrics, with an accuracy of 98%. Therefore, a tree-based model would be suitable for building an application and implementing it on smartphones. This resource would be available to healthcare professionals such as doctors and nurses.
Identifiants
pubmed: 38862945
doi: 10.1186/s12889-024-19083-8
pii: 10.1186/s12889-024-19083-8
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1573Subventions
Organisme : Coordenação de Aperfeiçoamento de Pessoal de Nível Superior , Brasil
ID : 001
Organisme : Coordenação de Aperfeiçoamento de Pessoal de Nível Superior , Brasil
ID : 001
Organisme : Coordenação de Aperfeiçoamento de Pessoal de Nível Superior , Brasil
ID : 001
Organisme : Coordenação de Aperfeiçoamento de Pessoal de Nível Superior , Brasil
ID : 001
Informations de copyright
© 2024. The Author(s).
Références
Huang SW, Tsai HP, Hung SJ, Ko WC, Wang JR. Assessing the risk of dengue severity using demographic information and laboratory test results with machine learning. PLoS Negl Trop Dis. 2020;14(12):e0008960. https://doi.org/10.1371/journal.pntd.0008960 .
doi: 10.1371/journal.pntd.0008960
pubmed: 33362244
pmcid: 7757819
Salim NAM, Wah YB, Reeves C, Smith M, Yaacob WFW, Mudin RN, Dapari R, Sapri N, Haque U. Prediction of dengue outbreak in Selangor Malaysia using machine learning techniques. Sci Rep. 2021;11(1):939. https://doi.org/10.1038s41598-020-791932.
doi: 10.1038/s41598-020-79193-2
pubmed: 33441678
pmcid: 7806812
Harapan H, Michie A, Sasmono RT, Imrie A. Dengue: a minireview. Viruses. 2020;12(8):829. https://doi.org/10.3390/v12080829 .
doi: 10.3390/v12080829
pubmed: 32751561
pmcid: 7472303
Marques CA, Siqueira MM, Portugal FB. Assessment of the lack of completeness of compulsory dengue fever notifications registered by a small municipality in Brazil. Ciênc saúde Coletiva. 2020;25(3):891–901. https://doi.org/10.1590/1413-81232020253.16162018 .
doi: 10.1590/1413-81232020253.16162018
Brasil. Ministério Da Saúde. Secretaria De Vigilância em Saúde. Departamento De Vigilância das Doenças Transmissíveis. Dengue: diagnóstico e manejo clínico: adulto e criança. 5 ed. Brasília: Ministério da Saúde; 2016.
Stanaway JD, Shepard DS, Undurraga EA, Halasa YA, Coffeng LE, Brady OJ, Murray CJ. The global burden of dengue: an analysis from the global burden of Disease Study 2013. Lancet Infect Dis. 2016;16(6):712–23. https://doi.org/10.1016/s1473-3099(16)00026-8 .
doi: 10.1016/s1473-3099(16)00026-8
pubmed: 26874619
pmcid: 5012511
Messina JP, Brady OJ, Golding N, Kraemer MU, Wint GW, Ray SE, Hay SI. The current and future global distribution and population at risk of dengue. Nat Microbiol. 2019;4(9):1508–15. https://doi.org/10.1038/s41564-019-0476-8 .
doi: 10.1038/s41564-019-0476-8
pubmed: 31182801
pmcid: 6784886
Zhao N, Charland K, Carabali M, Nsoesie EO, Maheu-Giroux M, Rees E, Yuan M, Garcia Balaguera C, Jaramillo Ramirez G, Zinszer K. Machine learning and dengue forecasting: comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in Colombia. PLoS Negl Trop Dis. 2020;14(9):e0008056. https://doi.org/10.1371/journal.pntd.0008056 .
doi: 10.1371/journal.pntd.0008056
pubmed: 32970674
pmcid: 7537891
Organização Pan-Americana da Saúde. (2024). Atualização epidemiológica - Aumento dos casos de dengue na Região das Américas – 29 de março de 2024. Washington, D.C.: OPAS/OMS; Disponível em: https://www.paho.org/pt/documentos/atualizacao-epidemiologica-aumento-dos-casos-dengue-na-regiao-das-americas-29-marco-2024 Acesso em: 16 May 2024.
Caicedo DM, Méndez AC, Tovar JR, Osorio L. Desarrollo De Algoritmos clínicos Para El diagnóstico del dengue en Colombia. Biomédica. 2019;39(1):170–85. https://doi.org/10.7705/biomedica.v39i2.3990 .
doi: 10.7705/biomedica.v39i2.3990
pubmed: 31021556
Ko HY, Salem GM, Chang GJJ, Chao DY. Application of next-generation sequencing to reveal how evolutionary dynamics of viral population shape dengue epidemiology. Front Microbiol. 2020;11:1371. https://doi.org/10.3389/fmicb.2020.01371 .
doi: 10.3389/fmicb.2020.01371
pubmed: 32636827
pmcid: 7318875
Khan W, Rahman A, Zaman S, Kabir M, Khan R, Ali W, Ahmad S, Shabir S, Jamil S, Ríos-Escalante P. D. los. Knowledge, attitude and practices regarding dengue and its vector among medical practitioners in Malakand region, Pakistan. Brazilian J Biology. 2023;83. https://doi.org/10.1590/1519-6984.244966 .
Brasil. Ministério da Saúde. Biblioteca Virtual da Saúde. OMS pede investimentos no combate a doenças tropicais negligenciadas Disponível em: https://bvsms.saude.gov.br/oms-pede-investimentos-no-combate-a-doencas-tropicais-negligenciadas Acesso em: 19 April 2023.
Davi C, Pastor A, Oliveira T, de Lima Neto FB, Braga-Neto U, Bigham AW, Acioli-Santos B. Severe dengue prognosis using human genome data and machine learning. IEEE Trans Biomed Eng. 2019;66(10):2861–8. https://doi.org/10.1109/TBME.2019.2897285 .
doi: 10.1109/TBME.2019.2897285
pubmed: 30716030
Khosavanna RR, Kareko BW, Brady AC, Booty BL, Nix CD, Lyski ZL, Curlin MD, Messer WB. Clinical symptoms of Dengue Infection among patients from a non-endemic area and potential for a predictive model: a multiple logistic regression analysis and decision tree. Am J Trop Med Hyg. 2021;104(1):121–9. https://doi.org/10.4269/ajtmh.20-0192 .
doi: 10.4269/ajtmh.20-0192
pubmed: 33200724
Tanner L, Schreiber M, Low JG, Ong A, Tolfvenstam T, Lai YL, Ng LC, Leo YS, Puong T, Vasudevan L, Simmons SG, Hibberd CP, M. L., Ooi EE. Decision tree algorithms predict the diagnosis and outcome of dengue fever in the early phase of illness. PLoS Negl Trop Dis. 2008;2(3):e196. https://doi.org/10.1371/journal.pntd.0000196 .
doi: 10.1371/journal.pntd.0000196
pubmed: 18335069
pmcid: 2263124
Saito CK, Machado SCP, Medina WSG, Paschoalato ABP. Sorologia E avaliação clínica: correlação no diagnóstico da dengue. Cuidarte Enferm Catanduva. 2017;1(11):72–7.
Nejad FY, Varathan KD. Identification of significant climatic risk factors and machine learning models in dengue outbreak prediction. BMC Med Inf Decis Mak. 2021;141. https://doi.org/10.1186/s12911-021-01493-y .
Ferreira ACBH, Ferreira DD, Barbosa BHG, Aline de Oliveira U, Padua A, Chiarini EO. F., & Baena de Moraes Lopes, M. H. Neural network-based method to stratify people at risk for developing diabetic foot: A support system for health professionals. Plos one. 2023;18(7), e0288466. https://doi.org/10.1371/journal.pone.0288466 .
Favan JR, dos Santos Coscolin RB, Jim AS, Gomes RL, de Passos S, J. R. Modelos computacionais e estatísticos para a predição da severidade da mancha foliar causada por Xanthomonas spp. em clone híbrido de Eucalyptus grandis x Eucalyptus urophyla. Tekhne E Logos. 2020;11(2):50–65.
Camargo AP, Duarte JC. Avaliando a Utilização do Aprendizado De Máquina em um Sistema De Apoio à Predição De Diagnósticos Médicos. Anais Estendidos do XV Simpósio Brasileiro De Sistemas Colaborativos. SBC; 2019. pp. 81–6.
Morelli AVR, Silva L. (2019). Análise epidemiológica: algoritmos de aprendizado de máquina para classificação de doenças.
Hoyos W, Aguilar J, Toro M. Dengue models based on machine learning techniques: a systematic literature review. Artif Intell Med. 2021;119:102157. https://doi.org/10.1016/j.artmed.2021.102157 .
doi: 10.1016/j.artmed.2021.102157
pubmed: 34531010
de Silveira V, F. R., Moreira LYMR. Utilização De Algoritmos De Aprendizagem De Máquina na Predição De Arboviroses transmitidas pelo Aedes Aegypti. Conexões-Ciência e Tecnologia. 2020;14(1):64–71.
doi: 10.21439/conexoes.v14i1.1824
de Paulo PHA, Stevanato KP, Christinell HCB, Westphal G, Costa MAR, da Silva Alexandrino WG. Desenvolvimento de ferramenta para a triagem de Dengue e COVID-19 na Atenção Primária à Saúde. Revista Enfermagem Atual In Derme. 2022;96(40).
Ministério da Saúde, Brasil. Sistema de Informação de Agravos de Notificação (Sinan Net). (2015). Disponível em: http://portalsinan.saude.gov.br/images/documentos/Agravos/Dengue/DIC_DADOS_ONLINE.pdf Acesso em: 17 abr. 2023.
Ministério da Saúde. DATASUS, Tabnet Brasília, DF: Ministério da Saúde, Disponível em: http://www.datasus.gov.br Acesso em: 17 abr. 2023.
Ross BC. Mutual information between discrete and continuous data sets. PLoS ONE. 2014;9(2):e87357. https://doi.org/10.1371/journal.pone.0087357 .
doi: 10.1371/journal.pone.0087357
pubmed: 24586270
pmcid: 3929353
McKinney W. (2010). Estruturas de dados para computação estatística em python. In Proceedings of the 9th Python in Science Conference, 445, 1.
Harris, C. R., Millman, K. J., Van Der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Oliphant, T. E. (2020). Array programming with NumPy. Nature. 2020;585(7825), 357–362. https://doi.org/10.1038/s41586-020-2649-2.
Hunter JD. Matplotlib: a 2D graphics environment. Comput Sci Eng. 2007;9:3, 90–5. https://doi.org/10.1109/MCSE.2007.55 .
doi: 10.1109/MCSE.2007.55
Raschka S, Mirjalili V. Python machine learning: machine learning and deep learning with python, scikit-learn, and tensorflow. 2nd ed. Birmingham: Packt Publishing; 2017.
Hyvärinen A, Kahunen J, Oja E. Independent component analysis. New York: John Wiley & Sons. Inc.; 2001. pp. 165–202.
doi: 10.1002/0471221317
Vergara JR, Estévez PA. A review of feature selection methods based on mutual information. Neural Comput Appl. 2014;24:175–86. https://doi.org/10.1007/s00521-013-1368-0 .
doi: 10.1007/s00521-013-1368-0
Zhong J, Wang J, Peng W, Zhang Z, Li M. A feature selection method for prediction essential protein. Tsinghua Sci Technol. 2015;20(5):491–9. https://doi.org/10.1109/TST.2015.7297748 .
doi: 10.1109/TST.2015.7297748
Pan Y, Xu W, Ran Q. An incremental approach to feature selection using the weighted dominance-based neighborhood rough sets. Int J Mach Learn Cybernet. 2023;14:1217–33. https://doi.org/10.1007/s13042-022-01695-4 .
doi: 10.1007/s13042-022-01695-4
Lu H, Xin MA. Modelos híbridos De Aprendizado De máquina baseados em árvore de decisão para previsão de qualidade da água em curto prazo. Chemosphere. 2020;249:126169.
doi: 10.1016/j.chemosphere.2020.126169
pubmed: 32078849
Han MKJ, Pei J. Data mining: concepts and techniques. 3 ed. Waltham, USA: Morgan Kaufmann; 2011.
Itoo F, Meenakshi, Singh S. Comparison and analysis of logistic regression, Naïve Bayes and KNN machine learning algorithms for credit card fraud detection. Int J Inform Technol. 2020;13(4):1503–11. https://doi.org/10.1007/s41870-020-00430-y .
doi: 10.1007/s41870-020-00430-y
James G, Witten D, Hastie T, Tibshirani R, Taylor J. Statistical learning. An introduction to statistical learning: with applications in Python. Cham: Springer International Publishing; 2023. pp. 15–67.
doi: 10.1007/978-3-031-38747-0_2
Haykin S. Redes neurais: princípios e prática. Bookman Editora; 2001.
Ludermir TB. Inteligência Artificial E Aprendizado De Máquina: estado atual e tendências. Estudos Avançados. 2021;35(101):85–94. https://doi.org/10.1590/s0103-4014.2021.35101.007 .
doi: 10.1590/s0103-4014.2021.35101.007
Santos HGD, Nascimento CFD, Izbicki R, Duarte YADO, Filho C, P., Dias A. Machine learning para análises preditivas em saúde: exemplo de aplicação para predizer óbito em idosos de São Paulo, Brasil. Cadernos De saúde pública. 2019;35:e00050818. https://doi.org/10.1590/0102-311X00050818 .
doi: 10.1590/0102-311X00050818
pubmed: 31365698
Izbicki R, dos Santos TM. (2020). Aprendizado de máquina: uma abordagem estatística Rafael Izbicki.
Jr EA, Fornaciali M, Batista A, Gazzola M, da Silva LP, Patrão DF, Jr MF. (2020). Utilização de Inteligência Artificial em Saúde.
Mendes MD, Santiago TC, Freire AS, Mayara NLL, Alberto SCC. Uma Ferramenta De Triagem E Orientação Nutricional Remota Durante a Pandemia De COVID-19. Revista Extensão. 2022;5(4):78–81.