Heart failure artificial intelligence-based computer aided diagnosis telecare system

  • Authors:

  • Gabriele Guidi;Ernesto Iadanza;Maria Chiara Pettenati;Massimo Milli;Francesco Pavone;Guido Biffi Gentili

  • Affiliations:

  • Electronics and Telecommunications Dept., Università degli Studi di Firenze, Italy;Electronics and Telecommunications Dept., Università degli Studi di Firenze, Italy;ICON (International Center of Computational Neurophotonics) Foundation, Florence, Italy;Dept. of Cardiology, Heart Failure Unit, Florence, Italy;ICON (International Center of Computational Neurophotonics) Foundation, Florence, Italy;Electronics and Telecommunications Dept., Università degli Studi di Firenze, Italy

  • Venue:
  • ICOST'12 Proceedings of the 10th international smart homes and health telematics conference on Impact Ananlysis of Solutions for Chronic Disease Prevention and Management
  • Year:
  • 2012

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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.