Chikungunya diagnosis using artificial intelligence and medical record data
DOI:
https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1372Keywords:
Artificial intelligence, Chikungunya fever, Clinical diagnosisAbstract
Objective: The purpose of this research is to develop and evaluate a machine learning model to assist in the clinical diagnosis of chikungunya fever using patient medical records. Method: Data were obtained from the Open Data Portal of Recife City Hall, encompassing 18,881 patient records. Results: After preprocessing and cross-validation, the Random Forest model with 100 trees showed the best performance, with an accuracy of 93.40% and a receiver-operator characteristic area of 0.990. The model application demonstrated high efficacy in differentiating between chikungunya and other conditions. Conclusion: We conclude that the use of artificial intelligence can significantly improve the clinical diagnosis of arboviruses. Future work includes expanding the database, integrating the model into clinical environments, and exploring advanced machine learning techniques.
References
Lopes N, Nozawa C, Linhares REC. Características gerais e epidemiologia dos arbovírus emergentes no Brasil. Rev Pan-Amaz Saude. 2014;5(3):10-10. DOI: https://doi.org/10.5123/S2176-62232014000300007
Donalisio MR, Freitas ARR, Zuben APBV. Arboviroses emergentes no Brasil: desafios para a clínica e implicações para a saúde pública. Rev Saude Publica. 2017;51:30. DOI: https://doi.org/10.1590/s1518-8787.2017051006889
Viana LRDC, Pimenta CJL, Araújo EMNFD, Teófilo TJL, Costa TFD, Costa KNDFM. Arboviroses reemergentes: perfil clínico-epidemiológico de idosos hospitalizados. Rev Esc Enferm USP. 2018;52:e03403. DOI: https://doi.org/10.1590/s1980-220x2017052103403
Young PR. Arboviruses: a family on the move. Dengue and Zika: control and antiviral treatment strategies. 2018:1-10. DOI: https://doi.org/10.1007/978-981-10-8727-1_1
Thisyakorn U, Thisyakorn C. Diseases caused by arboviruses‐dengue haemorrhagic fever and Japanese B encephalitis. Med J Aust. 1994;160(1):22-26. DOI: https://doi.org/10.5694/j.1326-5377.1994.tb138196.x
Bautista-Reyes E, Núñez-Avellaneda D, Alonso-Palomares L A, Salazar MI. Chikungunya: Molecular aspects, clinical outcomes and pathogenesis. Rev Investig Clin. 2018;69(6):299-307. DOI: https://doi.org/10.24875/RIC.17002029
Ahmed M, Mahboob N, Manun KT, Iqbal H. Dilemma in laboratory diagnosis of Dengue, Chikungunya and Zika viruses. Bangladesh J. 2019;30:83-92. DOI: https://doi.org/10.3329/bjmed.v30i2.41535
Cunha RVD, Trinta KS. Chikungunya virus: clinical aspects and treatment-A Review. Mem Inst Oswaldo Cruz. 2017;112(8):523-531. DOI: https://doi.org/10.1590/0074-02760170044
Santos MK, Webber CG. Comparing Deep e Transfer Learning na Classificação de Imagens da Membrana Timpânica. J Health Inform. 2022;14.
Santus E, Marino N, Cirillo D, Chersoni E, Montagud A, Santuccione Chadha A, et al. Artificial intelligence–aided precision medicine for COVID-19: strategic areas of research and development. J Med Internet Res. 2021;23(3):e22453. DOI: https://doi.org/10.2196/22453
Malik YS, Sircar S, Bhat S, Ansari MI, Pande T, Kumar P, et al. How artificial intelligence may help the Covid‐19 pandemic: Pitfalls and lessons for the future. Rev Med Virol. 2021;31(5):1-11. DOI: https://doi.org/10.1002/rmv.2205
da Silva Neto SR, Tabosa Oliveira T, Teixeira IV, Aguiar de Oliveira SB, Souza Sampaio V, Lynn T, et al. Machine learning and deep learning techniques to support clinical diagnosis of arboviral diseases: A systematic review. PLoS Negl Trop Dis. 2022;16(1):e0010061. DOI: https://doi.org/10.1371/journal.pntd.0010061
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Submission of a paper to Journal of Health Informatics is understood to imply that it is not being considered for publication elsewhere and that the author(s) permission to publish his/her (their) article(s) in this Journal implies the exclusive authorization of the publishers to deal with all issues concerning the copyright therein. Upon the submission of an article, authors will be asked to sign a Copyright Notice. Acceptance of the agreement will ensure the widest possible dissemination of information. An e-mail will be sent to the corresponding author confirming receipt of the manuscript and acceptance of the agreement.
