2017
Pereira, J.; Peixoto, H.; Machado, J.; Abelha, A.
A Data Mining Approach for Cardiovascular Diagnosis Journal Article
Em: Open Computer Science, vol. 7, não 1, pp. 36-40, 2017, ISSN: 22991093, (cited By 7).
Resumo | Links | BibTeX | Etiquetas: Accidents; Decision making; Diagnosis; Health care; Hospitals; Machine learning, Cardio-vascular disease; Cardiovascular diagnosis; Healthcare industry; Knowledge analysis; Large amounts; Large amounts of data; Machine learning methods; Quality of life, Data mining
@article{Pereira201736,
title = {A Data Mining Approach for Cardiovascular Diagnosis},
author = {J. Pereira and H. Peixoto and J. Machado and A. Abelha},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85052011971&doi=10.1515%2fcomp-2017-0007&partnerID=40&md5=162e5f28bc83a0bc23ae1549d6584df6},
doi = {10.1515/comp-2017-0007},
issn = {22991093},
year = {2017},
date = {2017-01-01},
journal = {Open Computer Science},
volume = {7},
number = {1},
pages = {36-40},
publisher = {Walter de Gruyter GmbH},
abstract = {The large amounts of data generated by healthcare transactions are too complex and voluminous to be processed and analysed by traditional methods. Data mining can improve decision-making by discovering patterns and trends in large amounts of complex data. In the healthcare industry specifically, data mining can be used to decrease costs by increasing efficiency, improve patient quality of life, and perhaps most importantly, save the lives of more patients. The main goal of this project is to apply data mining techniques in order to make possible the prediction of the degree of disability that patients will present when they leave hospitalization. The clinical data that will compose the data set was obtained from one single hospital and contains information about patients who were hospitalized in Cardio Vascular Disease’s (CVD) unit in 2016 for having suffered a cardiovascular accident. To develop this project, it will be used the Waikato Environment for Knowledge Analysis (WEKA) machine learning Workbench since this one allows users to quickly try out and compare different machine learning methods on new data sets. © 2017 J. Pereira et al},
note = {cited By 7},
keywords = {Accidents; Decision making; Diagnosis; Health care; Hospitals; Machine learning, Cardio-vascular disease; Cardiovascular diagnosis; Healthcare industry; Knowledge analysis; Large amounts; Large amounts of data; Machine learning methods; Quality of life, Data mining},
pubstate = {published},
tppubtype = {article}
}
The large amounts of data generated by healthcare transactions are too complex and voluminous to be processed and analysed by traditional methods. Data mining can improve decision-making by discovering patterns and trends in large amounts of complex data. In the healthcare industry specifically, data mining can be used to decrease costs by increasing efficiency, improve patient quality of life, and perhaps most importantly, save the lives of more patients. The main goal of this project is to apply data mining techniques in order to make possible the prediction of the degree of disability that patients will present when they leave hospitalization. The clinical data that will compose the data set was obtained from one single hospital and contains information about patients who were hospitalized in Cardio Vascular Disease’s (CVD) unit in 2016 for having suffered a cardiovascular accident. To develop this project, it will be used the Waikato Environment for Knowledge Analysis (WEKA) machine learning Workbench since this one allows users to quickly try out and compare different machine learning methods on new data sets. © 2017 J. Pereira et al