Research Article
Predicting Diabetes Disease for healthy smart cities
@ARTICLE{10.4108/eetsc.v6i18.589, author={Hugo Peixoto and Vasco Ramos and Carolina Marques and Jos\^{e} Machado}, title={Predicting Diabetes Disease for healthy smart cities}, journal={EAI Endorsed Transactions on Smart Cities}, volume={6}, number={18}, publisher={EAI}, journal_a={SC}, year={2022}, month={4}, keywords={Data Mining, Diabetes, CRISP-DM, Classification, ML Models, Smart Cities, Smart Health}, doi={10.4108/eetsc.v6i18.589} }- Hugo Peixoto
Vasco Ramos
Carolina Marques
José Machado
Year: 2022
Predicting Diabetes Disease for healthy smart cities
SC
EAI
DOI: 10.4108/eetsc.v6i18.589
Abstract
INTRODUCTION: Diabetes is a chronic condition that affects a large portion of the population and is the leading cause of numerous health problems. Its automatic detection could improve the communities’ overall well-being. OBJECTIVES: The primary goal was to introduce advancements to the subject of healthy smart cities by studying an approach for predicting the occurrence of diabetes in the Pima Female Adult Population using data mining. METHODS: This study uses CRISP-DM to analyze the results of six different models acquired from three different iterations of the same dataset. DISCUSSION: This study found that the most promising model is k-NN, which obtained results of almost 92% of F1 Score with the third data preparation strategy. CONCLUSION: Acceptable results were achieved with the k-NN model and the third data preparation strategy, but more research into improving the data preparation processes and their influence on the outputs of each model is needed.
Copyright © 2022 Hugo Peixoto et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
