DIABETES PREDICTION USING MACHINE LEARNING TECHNIQUES: A SCOPE STUDY
DOI:
https://doi.org/10.47820/recima21.v6i8.6727Keywords:
Scoping Study, Machine Learning, DiabetesAbstract
The use of Machine Learning techniques for disease prediction, such as diabetes, has gained prominence due to their ability to identify patterns in large volumes of medical data and assist in clinical decision-making. Diabetes is a chronic condition that, if not diagnosed and treated early, can lead to severe complications. Therefore, predicting its development in early stages, based on factors such as age, family history, lifestyle habits, and laboratory tests, can significantly contribute to health management and personalized treatment. In this context, this study investigates the application of Machine Learning algorithms in medical data analysis to predict the occurrence of the disease. Furthermore, this research emphasizes the importance of data quality, relevant feature selection, and model interpretability—essential factors for the reliability of predictions and their impact on medical decisions. Additionally, the study compares the performance of different algorithms, analyzing the contribution of each variable used and highlighting the most influential factors in diabetes prediction. Thus, this research aims not only to develop effective models but also to provide insights that can enhance predictive approaches in healthcare.
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ABDELHAFEZ, Hoda A.; AMER, Abeer A. Machine Learning techniques for diabetes prediction: A comparative analysis. Journal of Applied Data Sciences, [s. l.], v. 5, ed. 2, p. 792-807, 2024. DOI: https://doi.org/10.47738/jads.v5i2.219
ARKSEY, H.; O'MALLEY, L. Scoping studies: towards a methodological framework. International journal of social research methodology, v. 8, n. 1, p. 19-32, 2005. DOI: https://doi.org/10.1080/1364557032000119616
BHAT, S. S.; BANU, M.; ANSARI, G. A.; SELVAM, V. A risk assessment and prediction framework for diabetes mellitus using machine learning algorithms. Healthcare Analytics, [s. l.], ed. 4, 2023. DOI: https://doi.org/10.1016/j.health.2023.100273
BRASIL DIABETES REPORT. IDF Diabetes Atlas. 10. ed. [S. l.]: International Diabetes Federation (IDF), 2021. Disponível em: https://diabetesatlas.org/data/en/. Acesso em: 14 fev. 2025.
CHOU, C.-Y.; HSU, D.-Y.; CHOU, C.-H. Predicting the onset of diabetes with Machine Learning methods. Journal of personalized medicine, [s. l.], v. 13, n. 3, 2023. DOI: https://doi.org/10.3390/jpm13030406
DIABETES.ORG. Brasil já tem cerca de 20 milhões de pessoas com diabetes. [S. l.]: Sociedade Brasileira de Diabetes, 31 jan. 2025. Disponível em: https://diabetes.org.br/brasil-ja-tem-cerca-de-20-milhoes-de-pessoas-com-diabetes/. Acesso em: 14 fev. 2025.
FEBRIAN, M. E.; FERDINAN, F. X.; SENDANI, G. P.; SURYANIGRUM, K. M.; YUNANDA, R. Diabetes prediction using supervised machine learning. Procedia Computer Science, [s. l.], v. 216, p. 21-30, 2023. DOI: https://doi.org/10.1016/j.procs.2022.12.107
HOSSAIN, E.; ALSHEHRI, M.; ALMAKDI, S.; HALAWANI, H.; RAHMAN, M. M.; RAHMAN, W.; JANNAT, S.; KAYSAR, N; MIA, S. Dm-Health App: Diabetes Diagnosis Using Machine Learning with Smartphone. Computers, Materials & Continua, [s. l.], v. 72, ed. 1, p. 1713-1746, 2022. DOI: https://doi.org/10.32604/cmc.2022.024822
KAHN, C. R.; WEIR, G. C.; KING, G. L.; JACOBSON, A. M.; MOSES, A. C.; SMITH, R. J. Joslin: diabetes melito. 14. ed. Porto Alegre: ArtMed, 2009. E-book. p.346.
KHANAM, J. J.; FOO, S. Y. A comparison of Machine Learning algorithms for diabetes prediction. ICT Express, Coreia do Sul, v. 7, ed. 4, p. 432-439, 2021. DOI: https://doi.org/10.1016/j.icte.2021.02.004
OLISAH, C. C.; SMITH, L.; SMITH, M. Diabetes mellitus prediction and diagnosis from a data preprocessing and machine learning perspective. Computer Methods and Programs in Biomedicine, [s. l.], v. 220, 2022. DOI: https://doi.org/10.1016/j.cmpb.2022.106773
SANTOS, V. S. Comparison and selection of machine learning algorithms for diabetes prediction: An exploratory quantitative study based on medical data analysis. [S. l.]: Seven Editora, 2024. p. 737–765. DOI: https://doi.org/10.56238/sevened2024.007-053
SERRA, H. O.; NASCIMENTO, V. S. Aprendizado de máquina para diagnóstico de diabetes mellitus. 2022. Trabalho de Conclusão de Curso (Curso Superior de Tecnologia em Informática para Negócios) – Faculdade de Tecnologia de São José do Rio Preto, São José do Rio Preto, 2022.
SONI, M.; VARMA, S. Diabetes prediction using Machine Learning techniques. International Journal of Engineering Research & Technology (IJERT), [s. l.], v. 9, ed. 9, p. 921-925, 2020. DOI: https://doi.org/10.2139/ssrn.3642877
SOUZA, M. Diagnóstico precoce da diabetes pode evitar cegueira, amputações e infartos, dizem especialistas. Agência Câmara de Notícias, [S. l.], 19 nov. 2020. Disponível em: https://www.camara.leg.br/noticias/708896-diagnostico-precoce-da-diabetes-pode-evitar-cegueira-amputacoes-e-infartos-dizem-especialistas/?utm_source=chatgpt.com. Acesso em: 14 fev. 2025.
WHO. Diabetes. [S. l.]: World Health Organization, 2024. Disponível em: https://www.who.int/news-room/fact-sheets/detail/diabetes. Acesso em: 16 jan. 2025.
ZHOU, B. et al. Worldwide trends in diabetes prevalence and treatment from 1990 to 2022: a pooled analysis of 1108 population-representative studies with 141 million participants. The Lancet, [s. l.], v. 404, ed. 10467, p. 2077-2093, 2024.
ZOU, Q.; QU, K.; LUO, Y.; YIN, D.; JU, Y.; TANG, H. Predicting diabetes mellitus with Machine Learning techniques. Frontiers in genetics, [s. l.], v. 9, n. 515, 2018. DOI: https://doi.org/10.3389/fgene.2018.00515
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