| dc.relation | AKIBA, Takuya; SANO, Shotaro; YANASE, Toshihiko; OHTA, Takeru; KOYAMA,
Masanori. Optuna: A Next-generation Hyperparameter Optimization Framework.
In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge
Discovery and Data Mining, [S.l.], p. 2623-2631, 2019.
ALBAHRI, Ahmed. et al. A systematic review of trustworthy and explainable artificial
intelligence in healthcare: assessment of quality, bias risk, and data fusion.
Information Fusion, [S.l.], v. 96, p. 156-191, 2023. DOI:
10.1016/j.inffus.2023.03.008.
ALI, Sajid. et al. Explainable artificial intelligence (XAI): what we know and what is left
to attain trustworthy artificial intelligence. Information Fusion, [S.l.], v. 99, p. 101805,
2023. DOI: 10.1016/j.inffus.2023.101805.
AN, Qi et al. A comprehensive review on machine learning in healthcare industry:
classification, restrictions, opportunities and challenges. Sensors, [S.l.], v. 23, n. 9, p.
4178, 2023. DOI: 10.3390/s23094178.
CASCELLA, Marco et al. Features, evaluation, and treatment of coronavirus
(COVID-19). STATPEARLS, Treasure Island, Flórida, EUA, 2023. Disponível em:
https://www.ncbi.nlm.nih.gov/books/NBK554776/. Acesso em: 20 de julho de 2024.
CERVANTES, Jair. et al. A comprehensive survey on support vector machine
classification: applications, challenges and trends. Neurocomputing, [S.l.], v. 408, p.
189-215, 2020. DOI: 10.1016/j.neucom.2019.10.118.
CHEN, Tianqi; GUESTRIN, Carlos. XGBoost: a scalable tree boosting system. In:
PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE
ON KNOWLEDGE DISCOVERY AND DATA MINING. São Francisco, California,
EUA, 2016. p. 785-794. Nova Iorque, NI: Association for Computing Machinery. DOI:
10.1145/2939672.2939785.
DEBJIT, Kumar et al. An improved machine-learning approach for COVID-19
prediction using Harris Hawks optimization and feature analysis using SHAP.
Diagnostics, [S.l.], v. 12, n. 5, p. 1023, 2022. DOI: 10.3390/diagnostics12051023.
EMAMI, Amir et al. Prevalence of underlying diseases in hospitalized patients with
COVID-19: a systematic review and meta-analysis. Archives of Academic
Emergency Medicine, [S.l.], v. 8, n. 1, p. e35, 2020.
FACELI, Katti et al. Inteligência Artificial: uma abordagem de aprendizado de
máquina. 2. ed. Rio de Janeiro: LTC, 2021. p. 1, 3, 26, 28, 29, 35, 36, 43, 62
FONSECA, Vinicius L. N. et al. Classification of Tropical Disease-carrying
Mosquitoes Using Deep Learning and SHAP. In: SIMPÓSIO BRASILEIRO DE
COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 23. , 2023, São Paulo/SP. Anais Do
XXIII Simpósio Brasileiro De Computação Aplicada À Saúde. Porto Alegre:
Sociedade Brasileira de Computação, 2023. p. 25-34. DOI:
10.5753/sbcas.2023.229406.
FREITAS, Carlos M.; BARCELLOS, Christovam; VILLELA, Daniel A. M. Observatório
Covid-19 Fiocruz: uma análise da evolução da pandemia de fevereiro de 2020 a abril
de 2022. Ciência & Saúde Coletiva, [S.l.], v. 28, n. 10, p. 2845-2855, 2023. DOI:
10.1590/1413-812320232810.10412023.
GUAN, Wei-jie et al. Clinical characteristics of coronavirus disease 2019 in China.
New England Journal of Medicine, [S.l.], v. 382, n. 18, p. 1708-1720, 2020. DOI:
10.1056/NEJMoa2002032.
HULSEN, Tim et al. From big data to better patient outcomes. Clinical Chemistry
and Laboratory Medicine (CCLM), [S.l.], v. 61, n. 4, p. 580-586, 2022. DOI:
10.1515/cclm-2022-1096.
HULSEN, Tim. Explainable artificial intelligence (XAI): concepts and challenges in
healthcare. AI, [S.l.], v. 4, n. 3, p. 652-666, 2023. DOI: 10.3390/ai4030034.
IGUAL, Laura; SEGUÍ, Santi. Regression Analysis. In: Introduction to Data Science.
Undergraduate Topics in Computer Science. Cham: Springer, 2017. p. 07. DOI:
10.1007/978-3-319-50017-1_6.
JIA, Zhenge et al. The importance of resource awareness in artificial intelligence for
healthcare. Nature Machine Intelligence, [S.l.], v. 5, p. 687-698, 2023. DOI:
10.1038/s42256-023-00670-0.
JIJO, Bahzad T.; ABDULAZEEZ, Adnan. M. Classification based on decision tree
algorithm for machine learning. Journal of Applied Science and Technology
Trends, [S.l.], v. 2, n. 1, p. 20-28, 2021. DOI: 10.38094/jastt20165.
KLOKNER, Sarah G. M. et al. Epidemiological profile and risk factors predictors of
COVID-19 in southern Brazil. Research, Society and Development, [S.l.], v. 10, n.
3, p. e17710313197, 2021. DOI: 10.33448/rsd-v10i3.13197.
KUMAR, Yogesh K. et al. Artificial intelligence in disease diagnosis: a systematic
literature review, synthesizing framework and future research agenda. J Ambient
Intell Human Comput 14, [S.l.], 8459–8486, 2023. DOI:
10.1007/s12652-021-03612-z
KUSHWAH, Jitendra S. et al. Comparative study of regressor and classifier with
decision tree using modern tools. Materials Today: Proceedings, [S.l.], v. 56, p.
3571-3576, 2022. Nota: First International Conference on Design and Materials. DOI:
10.1016/j.matpr.2021.11.635.
LEE, Eun Hak. Exploring transit use during COVID-19 based on XGB and SHAP
using smart card data. Journal of Advanced Transportation, [S.l.], v. 2022, n. 1, p.
6458371, 2022. DOI: 10.1155/2022/6458371.
LIU, Lianhua et al. Predictive model and risk analysis for peripheral vascular disease
in type 2 diabetes mellitus patients using machine learning and Shapley additive
explanation. Frontiers in Endocrinology, [S.l.], v. 15, 2024. DOI:
10.3389/fendo.2024.1320335.
LUNDBERG, Scott M.; LEE, Su-In. A unified approach to interpreting model
predictions. In: PROCEEDINGS OF THE 31ST INTERNATIONAL CONFERENCE
ON NEURAL INFORMATION PROCESSING SYSTEMS (NIPS 2017). Long Beach,
Califórnia, EUA, 2017. p. 4768-4777. Red Hook, NI: Curran Associates Inc.
MARINHO, Pedro R. D. et al. Covid-19 in Brazil: a sad scenario. Cytokine & Growth
Factor Reviews, [S.l.], v. 58, p. 51-54, 2021. DOI: 10.1016/j.cytogfr.2020.10.010
PAULI, Suellen T. Z. de; KLEINA, M.; BONAT, W. H. MULTILAYER PERCEPTRON
ARTIFICIAL NEURAL NETWORKS: AN APPROACH FOR LEARNING THROUGH
THE BAYESIAN FRAMEWORK. Brazilian Journal of Biometrics, [S.l.], v. 39, n. 1,
p. 45–59, 2021. DOI: 10.28951/rbb.v39i1.495.
RAINIO, Oona; TEUHO, Jarmo; KLÉN, Riku. Evaluation metrics and statistical tests
for machine learning. Scientific Reports, [S.l.], v. 14, p. 6086, 2024. DOI:
10.1038/s41598-024-56706-x.
RODRIGUES, Gustavo; KREUTZ, Diego. Modelo preditivo para classificação de
risco de óbito de pacientes com COVID-19 utilizando dados abertos. In:
SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 22. ,
2022, Teresina. Anais Do XXII Simpósio Brasileiro De Computação Aplicada À
Saúde. Porto Alegre: Sociedade Brasileira de Computação, 2022. p. 144-155. ISSN
2763-8952. DOI: 10.5753/sbcas.2022.222494.
ROMANO, Donato et al. Insights from explainable artificial intelligence of pollution
and socioeconomic influences for respiratory cancer mortality in Italy. Journal of
Personalized Medicine, [S.l.], v. 14, n. 4, p. 430, 2024. DOI: 10.3390/jpm14040430.
ROUQUAYROL, Maria Zélia; GURGEL, Marcelo. Rouquayrol: epidemiologia e
saúde. 8. ed. Rio de Janeiro: MedBook, 2018. 719 p. 70 - 73.
SARKER, Iqbal H. Machine learning: algorithms, real-world applications and
research directions. SN Computer Science, [S.l.], v. 2, p. 160, 2021. DOI:
10.1007/s42979-021-00592-x
SCHONLAU, Matthias; ZOU, Rosie Y. The random forest algorithm for statistical
learning. The Stata Journal, [S.l.], v. 20, n. 1, p. 3-29, 2020. DOI:
10.1177/1536867X20909688.
SHARMA, Anshika et al. COVID-19: A review on the novel coronavirus disease
evolution, transmission, detection, control and prevention. Viruses, Basel, v. 13, n. 2,
p. 202, 2021. DOI: 10.3390/v13020202.
SIDEY-GIBBONS, Jenni A. M. et al. Machine learning in medicine: a practical
introduction. BMC Medical Research Methodology, [S.l.], v. 19, p. 64, 2019. DOI:
10.1186/s12874-019-0681-4.
UDDIN, Shahadat et al. Comparative performance analysis of K-nearest neighbour
(KNN) algorithm and its different variants for disease prediction. Scientific Reports,
[S.l.], v. 12, p. 6256, 2022. DOI: 10.1038/s41598-022-10358-x.
ULRICHSEN, Felipe C.; SENA, Alexandre C.; PÔRTO, Luís C. M. S.; FIGUEIREDO,
Karla. Avaliação do Uso de IA para Auxiliar no Diagnóstico de Casos de
COVID-19 para Diferentes Surtos. In: SIMPÓSIO BRASILEIRO DE
COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 22. , 2022, Teresina. Anais Do XXII
Simpósio Brasileiro De Computação Aplicada À Saúde. Porto Alegre: Sociedade
Brasileira de Computação, 2022. p. 108-119. DOI: 10.5753/sbcas.2022.222467.
VANACORE, Amalia; PELLEGRINO, Maria S.; CIARDELLI, Armando C. Fair
evaluation of classifier predictive performance based on binary confusion matrix.
Computational Statistics, [S.l.], v. 39, p. 363-383, 2024. DOI:
10.1007/s00180-022-01301-9.
WHITE, Ryen W.; MARCHIONINI, Gary; MURESAN, Gheorghe. Evaluating
exploratory search systems. Information Processing and Management 44, 2
(2008), 433.
WOODMAN, Richard J. et al. A comprehensive review of machine learning
algorithms and their application in geriatric medicine: present and future. Aging
Clinical and Experimental Research, [S.l.], v. 35, p. 2363-2397, 2023. DOI:
10.1007/s40520-023-02552-2.
WORLD HEALTH ORGANIZATION. WHO Director-General's opening remarks at
the media briefing on COVID-19 - 11 March 2020. [S.l.], 2020. Disponível em:
https://www.who.int/dg/speeches/detail/who-director-general-s-opening-remarks-at-th
e-media-briefing-on-covid-19---11-march-2020. Acesso em 20 de julho de 2024.
ZHOU, Zhi-Hua. Machine learning. Tradução de Shaowu Liu. 1. ed. Singapura:
Springer Singapore. p. 2, 4, 63, 2021. DOI: 10.1007/978-981-15-1967-3. | pt_BR |
