2020
Pinto, A.; Ferreira, D.; Neto, C.; Abelha, A.; Machado, J.
Data mining to predict early stage chronic kidney disease Proceedings Article
Em: E.M., Yasar A. Shakshuki (Ed.): pp. 562-567, Elsevier B.V., 2020, ISSN: 18770509, (cited By 5; Conference of 11th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN 2020 ; Conference Date: 2 November 2020 Through 5 November 2020; Conference Code:166555).
Resumo | Links | BibTeX | Etiquetas: Chronic conditions; Chronic kidney disease; CRISP-DM; Cross industry; Kidney disease; Kidney function; Risk stratification, Computer science; Computers, Data mining
@inproceedings{Pinto2020562,
title = {Data mining to predict early stage chronic kidney disease},
author = {A. Pinto and D. Ferreira and C. Neto and A. Abelha and J. Machado},
editor = {Yasar A. Shakshuki E.M.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099880679&doi=10.1016%2fj.procs.2020.10.079&partnerID=40&md5=3e496eba589d1580b145c7043e27343e},
doi = {10.1016/j.procs.2020.10.079},
issn = {18770509},
year = {2020},
date = {2020-01-01},
journal = {Procedia Computer Science},
volume = {177},
pages = {562-567},
publisher = {Elsevier B.V.},
abstract = {Chronic Kidney Disease (CKD) is a condition characterized by a gradual loss of kidney function over time. In national and international guidelines, CKD is organized into different degrees of risk stratification using commonly available markers. It is usually asymptomatic in its early stages, and early detection is important to reduce future risks. This study used the CRISP-DM (Cross Industry Standard Process for Data Mining) methodology and the WEKA software to build a system that can classify the chronic condition of kidney disease based on accuracy, sensitivity, specificity and precision. The results obtained were considered satisfactory, achieving the most suitable result of 97.66% of accuracy, 96.13% of sensitivity, 98.78% of specificity and 98.31% of precision with the J48 algorithm. © 2020 The Authors. Published by Elsevier B.V.},
note = {cited By 5; Conference of 11th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN 2020 ; Conference Date: 2 November 2020 Through 5 November 2020; Conference Code:166555},
keywords = {Chronic conditions; Chronic kidney disease; CRISP-DM; Cross industry; Kidney disease; Kidney function; Risk stratification, Computer science; Computers, Data mining},
pubstate = {published},
tppubtype = {inproceedings}
}
Chronic Kidney Disease (CKD) is a condition characterized by a gradual loss of kidney function over time. In national and international guidelines, CKD is organized into different degrees of risk stratification using commonly available markers. It is usually asymptomatic in its early stages, and early detection is important to reduce future risks. This study used the CRISP-DM (Cross Industry Standard Process for Data Mining) methodology and the WEKA software to build a system that can classify the chronic condition of kidney disease based on accuracy, sensitivity, specificity and precision. The results obtained were considered satisfactory, achieving the most suitable result of 97.66% of accuracy, 96.13% of sensitivity, 98.78% of specificity and 98.31% of precision with the J48 algorithm. © 2020 The Authors. Published by Elsevier B.V.