Chapter title |
Exploiting expert systems in cardiology: a comparative study.
|
---|---|
Chapter number | 6 |
Book title |
GeNeDis 2014
|
Published in |
Advances in experimental medicine and biology, November 2014
|
DOI | 10.1007/978-3-319-09012-2_6 |
Pubmed ID | |
Book ISBNs |
978-3-31-909011-5, 978-3-31-909012-2
|
Authors |
Economou GP, Sourla E, Stamatopoulou KM, Syrimpeis V, Sioutas S, Tsakalidis A, Tzimas G, George-Peter K. Economou, Efrosini Sourla, Konstantina-Maria Stamatopoulou, Vasileios Syrimpeis, Spyros Sioutas, Athanasios Tsakalidis, Giannis Tzimas, Economou, George-Peter K., Sourla, Efrosini, Stamatopoulou, Konstantina-Maria, Syrimpeis, Vasileios, Sioutas, Spyros, Tsakalidis, Athanasios, Tzimas, Giannis |
Abstract |
An improved Adaptive Neuro-Fuzzy Inference System (ANFIS) in the field of critical cardiovascular diseases is presented. The system stems from an earlier application based only on a Sugeno-type Fuzzy Expert System (FES) with the addition of an Artificial Neural Network (ANN) computational structure. Thus, inherent characteristics of ANNs, along with the human-like knowledge representation of fuzzy systems are integrated. The ANFIS has been utilized into building five different sub-systems, distinctly covering Coronary Disease, Hypertension, Atrial Fibrillation, Heart Failure, and Diabetes, hence aiding doctors of medicine (MDs), guide trainees, and encourage medical experts in their diagnoses centering a wide range of Cardiology. The Fuzzy Rules have been trimmed down and the ANNs have been optimized in order to focus into each particular disease and produce results ready-to-be applied to real-world patients. |
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United States | 1 | 100% |
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Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 36 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Master | 7 | 19% |
Student > Bachelor | 6 | 17% |
Student > Ph. D. Student | 6 | 17% |
Other | 2 | 6% |
Professor | 2 | 6% |
Other | 7 | 19% |
Unknown | 6 | 17% |
Readers by discipline | Count | As % |
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Medicine and Dentistry | 11 | 31% |
Computer Science | 9 | 25% |
Engineering | 5 | 14% |
Nursing and Health Professions | 2 | 6% |
Chemistry | 1 | 3% |
Other | 1 | 3% |
Unknown | 7 | 19% |