Machine Learning Analysis in the Prediction of Diabetes Mellitus: A Systematic Review of the Literature

Marieta Marres-Salhuana; Victor Garcia-Rios; Michael Cabanillas-Carbonell

doi:10.1007/978-981-19-1610-6_30

Machine Learning Analysis in the Prediction of Diabetes Mellitus: A Systematic Review of the Literature

Marieta Marres-Salhuana
, Victor Garcia-Rios
, Michael Cabanillas-Carbonell

Dirección de Investigación, Innovación y Sostenibilidad de la UPN

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

1 Cita (Scopus)

Resumen

In recent years, diabetes mellitus has increased its prevalence in the global landscape, and currently, due to COVID-19, people with diabetes mellitus are the most likely to develop a critical picture of this disease. In this study, we performed a systematic review of 55 researches focused on the prediction of diabetes mellitus and its different types, collected from databases such as IEEE Xplore, Scopus, ScienceDirect, IOPscience, EBSCOhost and Wiley. The results obtained show that one of the models based on support vector machine algorithms achieved 100% accuracy in disease prediction. The vast majority of the investigations used the Weka platform as a modeling tool, but it is worth noting that the best-performing models were developed in MATLAB (100%) and RStudio (99%).

Idioma original	Inglés
Título de la publicación alojada	Proceedings of 7th International Congress on Information and Communication Technology - ICICT 2022
Editores	Xin-She Yang, Simon Sherratt, Nilanjan Dey, Amit Joshi
Páginas	351-361
Número de páginas	11
DOI	https://doi.org/10.1007/978-981-19-1610-6_30
Estado	Publicada - 2023
Evento	7th International Congress on Information and Communication Technology, ICICT 2022 - Virtual, Online Duración: 21 feb. 2022 → 24 feb. 2022

Serie de la publicación

Nombre	Lecture Notes in Networks and Systems
Volumen	448
ISSN (versión impresa)	2367-3370
ISSN (versión digital)	2367-3389

Conferencia

Conferencia	7th International Congress on Information and Communication Technology, ICICT 2022
Ciudad	Virtual, Online
Período	21/02/22 → 24/02/22

ODS de las Naciones Unidas

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10.1007/978-981-19-1610-6_30

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Marres-Salhuana, M., Garcia-Rios, V., & Cabanillas-Carbonell, M. (2023). Machine Learning Analysis in the Prediction of Diabetes Mellitus: A Systematic Review of the Literature. En X.-S. Yang, S. Sherratt, N. Dey, & A. Joshi (Eds.), Proceedings of 7th International Congress on Information and Communication Technology - ICICT 2022 (pp. 351-361). (Lecture Notes in Networks and Systems; Vol. 448). https://doi.org/10.1007/978-981-19-1610-6_30

Marres-Salhuana, Marieta ; Garcia-Rios, Victor ; Cabanillas-Carbonell, Michael. / Machine Learning Analysis in the Prediction of Diabetes Mellitus : A Systematic Review of the Literature. Proceedings of 7th International Congress on Information and Communication Technology - ICICT 2022. editor / Xin-She Yang ; Simon Sherratt ; Nilanjan Dey ; Amit Joshi. 2023. pp. 351-361 (Lecture Notes in Networks and Systems).

@inproceedings{f16821899d68490880351c7f238cdd13,

title = "Machine Learning Analysis in the Prediction of Diabetes Mellitus: A Systematic Review of the Literature",

abstract = "In recent years, diabetes mellitus has increased its prevalence in the global landscape, and currently, due to COVID-19, people with diabetes mellitus are the most likely to develop a critical picture of this disease. In this study, we performed a systematic review of 55 researches focused on the prediction of diabetes mellitus and its different types, collected from databases such as IEEE Xplore, Scopus, ScienceDirect, IOPscience, EBSCOhost and Wiley. The results obtained show that one of the models based on support vector machine algorithms achieved 100\% accuracy in disease prediction. The vast majority of the investigations used the Weka platform as a modeling tool, but it is worth noting that the best-performing models were developed in MATLAB (100\%) and RStudio (99\%).",

keywords = "Diabetes mellitus, Machine learning, Predictive, Systematic review",

author = "Marieta Marres-Salhuana and Victor Garcia-Rios and Michael Cabanillas-Carbonell",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 7th International Congress on Information and Communication Technology, ICICT 2022 ; Conference date: 21-02-2022 Through 24-02-2022",

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Marres-Salhuana, M, Garcia-Rios, V & Cabanillas-Carbonell, M 2023, Machine Learning Analysis in the Prediction of Diabetes Mellitus: A Systematic Review of the Literature. En X-S Yang, S Sherratt, N Dey & A Joshi (eds.), Proceedings of 7th International Congress on Information and Communication Technology - ICICT 2022. Lecture Notes in Networks and Systems, vol. 448, pp. 351-361, 7th International Congress on Information and Communication Technology, ICICT 2022, Virtual, Online, 21/02/22. https://doi.org/10.1007/978-981-19-1610-6_30

Machine Learning Analysis in the Prediction of Diabetes Mellitus: A Systematic Review of the Literature. / Marres-Salhuana, Marieta; Garcia-Rios, Victor; Cabanillas-Carbonell, Michael.
Proceedings of 7th International Congress on Information and Communication Technology - ICICT 2022. ed. / Xin-She Yang; Simon Sherratt; Nilanjan Dey; Amit Joshi. 2023. p. 351-361 (Lecture Notes in Networks and Systems; Vol. 448).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

TY - GEN

T1 - Machine Learning Analysis in the Prediction of Diabetes Mellitus

T2 - 7th International Congress on Information and Communication Technology, ICICT 2022

AU - Marres-Salhuana, Marieta

AU - Garcia-Rios, Victor

AU - Cabanillas-Carbonell, Michael

PY - 2023

Y1 - 2023

N2 - In recent years, diabetes mellitus has increased its prevalence in the global landscape, and currently, due to COVID-19, people with diabetes mellitus are the most likely to develop a critical picture of this disease. In this study, we performed a systematic review of 55 researches focused on the prediction of diabetes mellitus and its different types, collected from databases such as IEEE Xplore, Scopus, ScienceDirect, IOPscience, EBSCOhost and Wiley. The results obtained show that one of the models based on support vector machine algorithms achieved 100% accuracy in disease prediction. The vast majority of the investigations used the Weka platform as a modeling tool, but it is worth noting that the best-performing models were developed in MATLAB (100%) and RStudio (99%).

AB - In recent years, diabetes mellitus has increased its prevalence in the global landscape, and currently, due to COVID-19, people with diabetes mellitus are the most likely to develop a critical picture of this disease. In this study, we performed a systematic review of 55 researches focused on the prediction of diabetes mellitus and its different types, collected from databases such as IEEE Xplore, Scopus, ScienceDirect, IOPscience, EBSCOhost and Wiley. The results obtained show that one of the models based on support vector machine algorithms achieved 100% accuracy in disease prediction. The vast majority of the investigations used the Weka platform as a modeling tool, but it is worth noting that the best-performing models were developed in MATLAB (100%) and RStudio (99%).

KW - Diabetes mellitus

KW - Machine learning

KW - Predictive

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DO - 10.1007/978-981-19-1610-6_30

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AN - SCOPUS:85135875438

SN - 9789811916090

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BT - Proceedings of 7th International Congress on Information and Communication Technology - ICICT 2022

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Marres-Salhuana M, Garcia-Rios V, Cabanillas-Carbonell M. Machine Learning Analysis in the Prediction of Diabetes Mellitus: A Systematic Review of the Literature. En Yang XS, Sherratt S, Dey N, Joshi A, editores, Proceedings of 7th International Congress on Information and Communication Technology - ICICT 2022. 2023. p. 351-361. (Lecture Notes in Networks and Systems). doi: 10.1007/978-981-19-1610-6_30

Machine Learning Analysis in the Prediction of Diabetes Mellitus: A Systematic Review of the Literature

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