Automated diagnosis of coronary artery disease based on data mining and fuzzy modeling
| dc.contributor.author | Tsipouras, M. G. | en |
| dc.contributor.author | Exarchos, T. P. | en |
| dc.contributor.author | Fotiadis, D. I. | en |
| dc.contributor.author | Kotsia, A. P. | en |
| dc.contributor.author | Vakalis, K. V. | en |
| dc.contributor.author | Naka, K. K. | en |
| dc.contributor.author | Michalis, L. K. | en |
| dc.date.accessioned | 2015-11-24T19:33:51Z | |
| dc.date.available | 2015-11-24T19:33:51Z | |
| dc.identifier.issn | 1558-0032 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/23577 | |
| dc.rights | Default Licence | - |
| dc.subject | *Artificial Intelligence | en |
| dc.subject | Coronary Artery Disease/*diagnosis | en |
| dc.subject | *Decision Support Systems, Clinical | en |
| dc.subject | *Decision Support Techniques | en |
| dc.subject | Diagnosis, Computer-Assisted/*methods | en |
| dc.subject | *Fuzzy Logic | en |
| dc.subject | Greece | en |
| dc.subject | Humans | en |
| dc.subject | Information Storage and Retrieval/*methods | en |
| dc.subject | Pattern Recognition, Automated/methods | en |
| dc.subject | Reproducibility of Results | en |
| dc.subject | Sensitivity and Specificity | en |
| dc.title | Automated diagnosis of coronary artery disease based on data mining and fuzzy modeling | en |
| heal.abstract | A fuzzy rule-based decision support system (DSS) is presented for the diagnosis of coronary artery disease (CAD). The system is automatically generated from an initial annotated dataset, using a four stage methodology: 1) induction of a decision tree from the data; 2) extraction of a set of rules from the decision tree, in disjunctive normal form and formulation of a crisp model; 3) transformation of the crisp set of rules into a fuzzy model; and 4) optimization of the parameters of the fuzzy model. The dataset used for the DSS generation and evaluation consists of 199 subjects, each one characterized by 19 features, including demographic and history data, as well as laboratory examinations. Tenfold cross validation is employed, and the average sensitivity and specificity obtained is 62% and 54%, respectively, using the set of rules extracted from the decision tree (first and second stages), while the average sensitivity and specificity increase to 80% and 65%, respectively, when the fuzzification and optimization stages are used. The system offers several advantages since it is automatically generated, it provides CAD diagnosis based on easily and noninvasively acquired features, and is able to provide interpretation for the decisions made. | en |
| heal.access | campus | - |
| heal.fullTextAvailability | TRUE | - |
| heal.identifier.primary | 10.1109/TITB.2007.907985 | - |
| heal.identifier.secondary | http://www.ncbi.nlm.nih.gov/pubmed/18632325 | - |
| heal.journalName | IEEE Trans Inf Technol Biomed | en |
| heal.journalType | peer-reviewed | - |
| heal.language | en | - |
| heal.publicationDate | 2008 | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Επιστημών Υγείας. Τμήμα Ιατρικής | el |
| heal.type | journalArticle | - |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.type.en | Journal article | en |
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