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Title: A Soft Computing Approach to Acute Coronary Syndrome
Authors: Vicente, Henrique
Martins, M. Rosário
Mendes, Teresa
Vilhena, João
Grañeda, José
Gusmão, Rodrigo
Neves, José
Keywords: Artificial Neuronal Networks
Acute Coronary Syndrome
Acute Myocardial Infarction
Cardiovascular Disease Risk Assessment
Knowledge Representation and Reasoning
Logic Programming
Issue Date: 2016
Publisher: Austin Publishing Group
Citation: Vicente, H., Martins, M.R., Mendes, T., Vilhena, J., Grañeda, J., Gusmão, R. & Neves, J., A Soft Computing Approach to Acute Coronary Syndrome Risk Evaluation. Austin Journal of Clinical Cardiology, 3 (1): Article ID 1044, 8 pages, 2016.
Abstract: Acute Coronary Syndrome (ACS) is transversal to a broad and heterogeneous set of human beings, and assumed as a serious diagnosis and risk stratification problem. Although one may be faced with or had at his disposition different tools as biomarkers for the diagnosis and prognosis of ACS, they have to be previously evaluated and validated in different scenarios and patient cohorts. Besides ensuring that a diagnosis is correct, attention should also be directed to ensure that therapies are either correctly or safely applied. Indeed, this work will focus on the development of a diagnosis decision support system in terms of its knowledge representation and reasoning mechanisms, given here in terms of a formal framework based on Logic Programming, complemented with a problem solving methodology to computing anchored on Artificial Neural Networks. On the one hand it caters for the evaluation of ACS predisposing risk and the respective Degree-of-Confidence that one has on such a happening. On the other hand it may be seen as a major development on the Multi-Value Logics to understand things and ones behavior. Undeniably, the proposed model allows for an improvement of the diagnosis process, classifying properly the patients that presented the pathology (sensitivity ranging from 89.7% to 90.9%) as well as classifying the absence of ACS (specificity ranging from 88.4% to 90.2%).
ISSN: 2381-9111
Type: article
Appears in Collections:HERCULES - Publicações - Artigos em Revistas Internacionais Com Arbitragem Científica
QUI - Publicações - Artigos em Revistas Internacionais Com Arbitragem Científica

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