This paper is in the following e-collection/theme issue:

JMIR Theme Issue: COVID-19 Special Issue (2452) Machine Learning (1465) Outbreak and Pandemic Preparedness and Management (1727) Infectious Diseases (non-STD/STI) (1575) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1517) Artificial Intelligence (1371) Decision Support for Health Professionals (1185)

Published on in Vol 23, No 4 (2021): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/27293, first published .
Classification Models for COVID-19 Test Prioritization in Brazil: Machine Learning Approach

Classification Models for COVID-19 Test Prioritization in Brazil: Machine Learning Approach

Classification Models for COVID-19 Test Prioritization in Brazil: Machine Learning Approach

Authors of this article:

Íris Viana dos Santos Santana1 Author Orcid Image ;   Andressa CM da Silveira2 Author Orcid Image ;   Álvaro Sobrinho1, 3 Author Orcid Image ;   Lenardo Chaves e Silva4 Author Orcid Image ;   Leandro Dias da Silva3 Author Orcid Image ;   Danilo F S Santos2 Author Orcid Image ;   Edmar C Gurjão2 Author Orcid Image ;   Angelo Perkusich2 Author Orcid Image
Article Authors Cited by (23) Tweetations (2) Metrics

Journals

  1. Silveira A, Sobrinho Á, Silva L, Costa E, Pinheiro M, Perkusich A. Exploring Early Prediction of Chronic Kidney Disease Using Machine Learning Algorithms for Small and Imbalanced Datasets. Applied Sciences 2022;12(7):3673 View
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  4. Yang T, Chien T, Lai F. Web-Based Skin Cancer Assessment and Classification Using Machine Learning and Mobile Computerized Adaptive Testing in a Rasch Model: Development Study. JMIR Medical Informatics 2022;10(3):e33006 View
  5. Lin C, Chien T, Chen Y, Lee Y, Su S. An app to classify a 5-year survival in patients with breast cancer using the convolutional neural networks (CNN) in Microsoft Excel. Medicine 2022;101(4):e28697 View
  6. Monday H, Li J, Nneji G, Nahar S, Hossin M, Jackson J, Ejiyi C. COVID-19 Diagnosis from Chest X-ray Images Using a Robust Multi-Resolution Analysis Siamese Neural Network with Super-Resolution Convolutional Neural Network. Diagnostics 2022;12(3):741 View
  7. Nandy S, Adhikari M, Hazra A, Mukherjee T, Menon V. Analysis of Communicable Disease Symptoms Using Bag-of-Neural Network at Edge Networks. IEEE Sensors Journal 2023;23(2):914 View
  8. Struyf T, Deeks J, Dinnes J, Takwoingi Y, Davenport C, Leeflang M, Spijker R, Hooft L, Emperador D, Domen J, Tans A, Janssens S, Wickramasinghe D, Lannoy V, Horn S, Van den Bruel A. Signs and symptoms to determine if a patient presenting in primary care or hospital outpatient settings has COVID-19. Cochrane Database of Systematic Reviews 2022;2022(5) View
  9. Hassan A, Prasad D, Rani S, Alhassan M, Teekaraman Y. Gauging the Impact of Artificial Intelligence and Mathematical Modeling in Response to the COVID-19 Pandemic: A Systematic Review. BioMed Research International 2022;2022:1 View
  10. Nneji G, Cai J, Monday H, Hossin M, Nahar S, Mgbejime G, Deng J. Fine-Tuned Siamese Network with Modified Enhanced Super-Resolution GAN Plus Based on Low-Quality Chest X-ray Images for COVID-19 Identification. Diagnostics 2022;12(3):717 View
  11. Gürsoy E, Kaya Y. An overview of deep learning techniques for COVID-19 detection: methods, challenges, and future works. Multimedia Systems 2023;29(3):1603 View
  12. Hu T, Chow J, Chien T, Chou W. Detecting dengue fever in children using online Rasch analysis to develop algorithms for parents: An APP development and usability study. Medicine 2023;102(13):e33296 View
  13. Hassan A, Rashid T. A Hybrid Artificial Neural Network and Particle Swarm Optimization algorithm for Detecting COVID-19 Patients. Kurdistan Journal of Applied Research 2021:44 View
  14. Abbasi Habashi S, Koyuncu M, Alizadehsani R. A Survey of COVID-19 Diagnosis Using Routine Blood Tests with the Aid of Artificial Intelligence Techniques. Diagnostics 2023;13(10):1749 View
  15. Chadaga K, Prabhu S, Sampathila N, Chadaga R. Severity prediction in COVID-19 patients using clinical markers and explainable artificial intelligence: A stacked ensemble machine learning approach. Intelligent Decision Technologies 2023;17(4):959 View
  16. Chen X, Chen Q, Liu Y, Qiu Y, Lv L, Zhang Z, Yin X, Shu F. Radiomics models to predict bone marrow metastasis of neuroblastoma using CT. Cancer Innovation 2024;3(5) View
  17. Ariza-Colpas P, Piñeres-Melo M, Urina-Triana M, Barceló-Martinez E, Barceló-Castellanos C, Roman F. Machine Learning Applied to the Analysis of Prolonged COVID Symptoms: An Analytical Review. Informatics 2024;11(3):48 View
  18. Karabulut B, Arslan G, Ünver H. A NOVEL COVID-19 CLASSIFICATION METHOD BASED ON CURE CLUSTERING. Scientific Journal of Mehmet Akif Ersoy University 2024;7(1):25 View
  19. Rodríguez Mallma M, Zuloaga-Rotta L, Borja-Rosales R, Rodríguez Mallma J, Vilca-Aguilar M, Salas-Ojeda M, Mauricio D. Explainable Machine Learning Models for Brain Diseases: Insights from a Systematic Review. Neurology International 2024;16(6):1285 View

Books/Policy Documents

  1. Xu X, Ding X, Qin Z, Liu Y. Neural Information Processing. View
  2. Qi J, Burnside G, Coenen F. Big Data Analytics and Knowledge Discovery. View