Abstract
In this paper we present an Artificial Intelligence-based Computer Aided Diagnosis system designed to assist the clinical decision of non-specialist staff in the analysis of Heart Failure patients. The system computes the patient’s pathological condition and highlights possible aggravations. The system is based on three functional parts: Diagnosis (severity assessing), Prognosis, and Follow-up management. Four Artificial Intelligence-based techniques are used and compared in diagnosis function: a Neural Network, a Support Vector Machine, a Decision Tree and a Fuzzy Expert System whose rules are produced by a Genetic Algorithm. In order to offer a complete HF analysis dashboard, state of the art algorithms are implemented to support a score-based prognosis function. The patient’s Follow-up is used to refine the diagnosis by adding Heart Failure type information and to detect any worsening of patient’s clinical status. In the Results section we compared the accuracy of the different implemented techniques.
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Guidi, G., Iadanza, E., Pettenati, M.C., Milli, M., Pavone, F., Biffi Gentili, G. (2012). Heart Failure Artificial Intelligence-Based Computer Aided Diagnosis Telecare System. In: Donnelly, M., Paggetti, C., Nugent, C., Mokhtari, M. (eds) Impact Analysis of Solutions for Chronic Disease Prevention and Management. ICOST 2012. Lecture Notes in Computer Science, vol 7251. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30779-9_44
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DOI: https://doi.org/10.1007/978-3-642-30779-9_44
Publisher Name: Springer, Berlin, Heidelberg
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