2021
Vieira, E.; Ferreira, D.; Neto, C.; Abelha, A.; Machado, J.
Data Mining Approach to Classify Cases of Lung Cancer Proceedings Article
Em: A., Dzemyda G. Adeli H. Rocha (Ed.): pp. 511-521, Springer Science and Business Media Deutschland GmbH, 2021, ISSN: 21945357, (cited By 3; Conference of World Conference on Information Systems and Technologies, WorldCIST 2021 ; Conference Date: 1 April 2021 Through 2 April 2021; Conference Code:256979).
Resumo | Links | BibTeX | Etiquetas: Artificial neural network algorithm; Cancer prevention; Cancer research; Data mining models; Mining classification; Risk factors; Sampling method; Weight loss, Biological organs; Classification (of information); Diseases; Information systems; Information use; Neural networks, Data mining
@inproceedings{Vieira2021511,
title = {Data Mining Approach to Classify Cases of Lung Cancer},
author = {E. Vieira and D. Ferreira and C. Neto and A. Abelha and J. Machado},
editor = {Dzemyda G. Adeli H. Rocha A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105949008&doi=10.1007%2f978-3-030-72657-7_49&partnerID=40&md5=071cf3523dd9f477acf20273d8405f0e},
doi = {10.1007/978-3-030-72657-7_49},
issn = {21945357},
year = {2021},
date = {2021-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {1365 AIST},
pages = {511-521},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {According to the World Cancer Research Fund, a leading authority on cancer prevention research, lung cancer is the most commonly occurring cancer in men and the third most commonly occurring cancer in women, with the 5-year relative survival percentage being significantly low. Smoking is the major risk factor for lung cancer and the symptoms associated with it include cough, fatigue, shortness of breath, chest pain, weight loss, and loss of appetite. In an attempt to build a model capable of identifying individuals with lung cancer, this study aims to build a data mining classification model to predict whether or not a patient has lung cancer based on crucial features such as the above mentioned symptoms. Through the CRISP-DM methodology and the RapidMiner software, different models were built, using different scenarios, algorithms, sampling methods, and data approaches. The best data mining model achieved an accuracy of 93%, a sensitivity of 96%, a specificity of 90% and a precision of 91%, using the Artificial Neural Network algorithm. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
note = {cited By 3; Conference of World Conference on Information Systems and Technologies, WorldCIST 2021 ; Conference Date: 1 April 2021 Through 2 April 2021; Conference Code:256979},
keywords = {Artificial neural network algorithm; Cancer prevention; Cancer research; Data mining models; Mining classification; Risk factors; Sampling method; Weight loss, Biological organs; Classification (of information); Diseases; Information systems; Information use; Neural networks, Data mining},
pubstate = {published},
tppubtype = {inproceedings}
}
According to the World Cancer Research Fund, a leading authority on cancer prevention research, lung cancer is the most commonly occurring cancer in men and the third most commonly occurring cancer in women, with the 5-year relative survival percentage being significantly low. Smoking is the major risk factor for lung cancer and the symptoms associated with it include cough, fatigue, shortness of breath, chest pain, weight loss, and loss of appetite. In an attempt to build a model capable of identifying individuals with lung cancer, this study aims to build a data mining classification model to predict whether or not a patient has lung cancer based on crucial features such as the above mentioned symptoms. Through the CRISP-DM methodology and the RapidMiner software, different models were built, using different scenarios, algorithms, sampling methods, and data approaches. The best data mining model achieved an accuracy of 93%, a sensitivity of 96%, a specificity of 90% and a precision of 91%, using the Artificial Neural Network algorithm. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.