2023
Neto, C.; Ferreira, D.; Cunha, H.; Pires, M.; Marques, S.; Sousa, R.; Machado, J.
Recommendation of Medical Exams to Support Clinical Diagnosis Based on Patient’s Symptoms Proceedings Article
Em: J.M., Peixoto H. Machado (Ed.): pp. 91-100, Springer Science and Business Media Deutschland GmbH, 2023, ISSN: 18678211, (cited By 0; Conference of 3rd International Conference on AI-assisted Solutions for COVID-19 and Biometrical Applications in Smart Cities, AISCOVID-19 2022 ; Conference Date: 16 November 2022 Through 18 November 2022; Conference Code:298899).
Resumo | Links | BibTeX | Etiquetas: Clinical diagnosis; CRISP-DM; Diagnoses of disease; Health care professionals; Health professionals; Machine learning techniques; Medical areas; Medical conditions; Medical errors; Medical exam, Diagnosis; Learning systems; Nearest neighbor search, Support vector machines
@inproceedings{Neto202391,
title = {Recommendation of Medical Exams to Support Clinical Diagnosis Based on Patient’s Symptoms},
author = {C. Neto and D. Ferreira and H. Cunha and M. Pires and S. Marques and R. Sousa and J. Machado},
editor = {Peixoto H. Machado J.M.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172725999&doi=10.1007%2f978-3-031-38204-8_8&partnerID=40&md5=e29bde9aad6cf908bce88ef83007236a},
doi = {10.1007/978-3-031-38204-8_8},
issn = {18678211},
year = {2023},
date = {2023-01-01},
journal = {Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST},
volume = {485 LNICST},
pages = {91-100},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Nowadays, it is essential that the error in the decisions made by health professionals is as small as possible. This applies to any medical area, including the recommendation of medical exams based on certain symptoms for the diagnosis of diseases. This study aims to explore the use of different Machine Learning techniques to increase the confidence of the medical exams prescribed by healthcare professionals. A successful implementation of this proposal could reduce the probability of medical errors in what concerns the prescription of medical exams and, consequently, the diagnosis of medical conditions. Thus, in this paper, six Machine Learning models were applied and optimized, namely, RF, DT, k-NN, NB, SVM and RNN, in order to find the most suitable model for the problem at hand. The results obtained with this study were promising, achieving high accuracy values with RF, DT and k-NN. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.},
note = {cited By 0; Conference of 3rd International Conference on AI-assisted Solutions for COVID-19 and Biometrical Applications in Smart Cities, AISCOVID-19 2022 ; Conference Date: 16 November 2022 Through 18 November 2022; Conference Code:298899},
keywords = {Clinical diagnosis; CRISP-DM; Diagnoses of disease; Health care professionals; Health professionals; Machine learning techniques; Medical areas; Medical conditions; Medical errors; Medical exam, Diagnosis; Learning systems; Nearest neighbor search, Support vector machines},
pubstate = {published},
tppubtype = {inproceedings}
}