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.
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Acknowledgements
This work has been supported by FCT—Fundação para a Ciência e Tecnologia within the R &D Units Project Scope: UIDB/00319/2020. Diana Ferreira and Cristiana Neto thank the Fundação para a Ciência e Tecnologia (FCT) Portugal for the grants 2021.06308.BD and 2021.06507.BD, respectively. The grant of Regina Sousa is supported by the project “Integrated and Innovative Solutions for the well-being of people in complex urban centers” within the Project Scope NORTE-01-0145-FEDER-000086.
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Neto, C. et al. (2023). Recommendation of Medical Exams to Support Clinical Diagnosis Based on Patient’s Symptoms. In: Machado, J.M., Peixoto, H. (eds) AI-assisted Solutions for COVID-19 and Biomedical Applications in Smart Cities. AISCOVID 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 485. Springer, Cham. https://doi.org/10.1007/978-3-031-38204-8_8
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