2022
Marques, C.; Ramos, V.; Peixoto, H.; Machado, J.
Predicting Diabetes Disease in the Female Adult Population, Using Data Mining Proceedings Article
Em: S., Goleva R. Silva B. Spinsante (Ed.): pp. 63-73, Springer Science and Business Media Deutschland GmbH, 2022, ISSN: 18678211, (cited By 0; Conference of 8th EAI International Conference on IoT Technologies for Health-Care, HealthyIoT 2021 ; Conference Date: 24 November 2021 Through 26 November 2021; Conference Code:275359).
Resumo | Links | BibTeX | Etiquetas: Adult populations; Chronic disease; CRISP-DM; Data-mining techniques; Female adults; Heart attack; Logistics regressions; Lower-limb amputations; ML model; Naive bayes, Barium compounds; Decision trees; Insulin; Logistic regression; Nearest neighbor search; Population statistics, Data mining
@inproceedings{Marques202263,
title = {Predicting Diabetes Disease in the Female Adult Population, Using Data Mining},
author = {C. Marques and V. Ramos and H. Peixoto and J. Machado},
editor = {Goleva R. Silva B. Spinsante S.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127917985&doi=10.1007%2f978-3-030-99197-5_6&partnerID=40&md5=d0b1061ffb8b1c4ae3d164d82c9a4a30},
doi = {10.1007/978-3-030-99197-5_6},
issn = {18678211},
year = {2022},
date = {2022-01-01},
journal = {Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST},
volume = {432 LNICST},
pages = {63-73},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {The aim of this study is to predict, through data mining, the incidence of diabetes disease in the Pima Female Adult Population. Diabetes is a chronic disease that occurs either when the pancreas does not produce enough insulin or when the body cannot effectively use the insulin it produces and is a major cause of blindness, kidney failure, heart attacks, stroke and lower limb amputation. The information collected from this population combined with the data mining techniques, may help to detect earlier the presence of this decease. To achieve the best possible ML model, this work uses the CRISP-DM methodology and compares the results of five ML models (Logistic Regression, Naive Bayes, Random Forest, Gradient Boosted Trees and k-NN) obtained from two different datasets (originated from two different data preparation strategies). The study shows that the most promising model as k-NN, which produced results of 90% of accuracy and also 90% of F1 Score, in the most realistic evaluation scenario. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.},
note = {cited By 0; Conference of 8th EAI International Conference on IoT Technologies for Health-Care, HealthyIoT 2021 ; Conference Date: 24 November 2021 Through 26 November 2021; Conference Code:275359},
keywords = {Adult populations; Chronic disease; CRISP-DM; Data-mining techniques; Female adults; Heart attack; Logistics regressions; Lower-limb amputations; ML model; Naive bayes, Barium compounds; Decision trees; Insulin; Logistic regression; Nearest neighbor search; Population statistics, Data mining},
pubstate = {published},
tppubtype = {inproceedings}
}
The aim of this study is to predict, through data mining, the incidence of diabetes disease in the Pima Female Adult Population. Diabetes is a chronic disease that occurs either when the pancreas does not produce enough insulin or when the body cannot effectively use the insulin it produces and is a major cause of blindness, kidney failure, heart attacks, stroke and lower limb amputation. The information collected from this population combined with the data mining techniques, may help to detect earlier the presence of this decease. To achieve the best possible ML model, this work uses the CRISP-DM methodology and compares the results of five ML models (Logistic Regression, Naive Bayes, Random Forest, Gradient Boosted Trees and k-NN) obtained from two different datasets (originated from two different data preparation strategies). The study shows that the most promising model as k-NN, which produced results of 90% of accuracy and also 90% of F1 Score, in the most realistic evaluation scenario. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.