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
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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
KW - Systematic review
UR - http://www.scopus.com/inward/record.url?scp=85135875438&partnerID=8YFLogxK
U2 - 10.1007/978-981-19-1610-6_30
DO - 10.1007/978-981-19-1610-6_30
M3 - Conference contribution
AN - SCOPUS:85135875438
SN - 9789811916090
T3 - Lecture Notes in Networks and Systems
SP - 351
EP - 361
BT - Proceedings of 7th International Congress on Information and Communication Technology - ICICT 2022
A2 - Yang, Xin-She
A2 - Sherratt, Simon
A2 - Dey, Nilanjan
A2 - Joshi, Amit
Y2 - 21 February 2022 through 24 February 2022
ER -
