35
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Application of pattern recognition tools for classifying acute coronary syndrome: an integrated medical modeling

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Objective

          The classification of Acute Coronary Syndrome (ACS), using artificial intelligence (AI), has recently drawn the attention of the medical researchers. Using this approach, patients with myocardial infarction can be differentiated from those with unstable angina. The present study aims to develop an integrated model, based on the feature selection and classification, for the automatic classification of ACS.

          Methods

          A dataset containing medical records of 809 patients suspected to suffer from ACS was used. For each subject, 266 clinical factors were collected. At first, a feature selection was performed based on interviews with 20 cardiologists; thereby 40 seminal features for classifying ACS were selected. Next, a feature selection algorithm was also applied to detect a subset of the features with the best classification accuracy. As a result, the feature numbers considerably reduced to only seven. Lastly, based on the seven selected features, eight various common pattern recognition tools for classification of ACS were used.

          Results

          The performance of the aforementioned classifiers was compared based on their accuracy computed from their confusion matrices. Among these methods, the multi-layer perceptron showed the best performance with the 83.2% accuracy.

          Conclusion

          The results reveal that an integrated AI-based feature selection and classification approach is an effective method for the early and accurate classification of ACS and ultimately a timely diagnosis and treatment of this disease.

          Related collections

          Most cited references17

          • Record: found
          • Abstract: not found
          • Article: not found

          ANFIS: adaptive-network-based fuzzy inference system

            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            The second Euro Heart Survey on acute coronary syndromes: Characteristics, treatment, and outcome of patients with ACS in Europe and the Mediterranean Basin in 2004.

            Our study aimed to examine the management of acute coronary syndromes (ACS) in Europe and the Mediterranean basin, and to compare adherence to guidelines with that reported in the first Euro Heart Survey on ACS (EHS-ACS-I), 4 years earlier. In a prospective survey conducted in 2004 (EHS-ACS-II), data describing the characteristics, treatment, and outcome of 6385 patients diagnosed with ACS in 190 medical centres in 32 countries were collected. ACS with ST-elevation was the initial diagnosis in 47% of patients, no ST-elevation in 48%, and undetermined electrocardiographic pattern in 5% of patients. Comparison of data collected in 2000 and 2004 showed similar baseline characteristics, but greater use of recommended medications and coronary interventions in EHS-ACS-II. Among patients with ST-elevation, the use of primary reperfusion increased slightly (from 56 to 64%), with a significant shift from fibrinolytic therapy to primary percutaneous coronary intervention (PPCI). The use of PPCI rose from 37 to 59% among those undergoing primary reperfusion therapy. Analysis of data in 34 centres that participated in both surveys showed even greater improvement with respect to the use of recommended medical therapy, interventions, and outcome. Data from EHS-ACS-II suggest an increase in adherence to guidelines for treatment of ACS in comparison with EHS-ACS-I.
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Pattern analysis for machine olfaction: a review

                Bookmark

                Author and article information

                Contributors
                Journal
                Theor Biol Med Model
                Theor Biol Med Model
                Theoretical Biology & Medical Modelling
                BioMed Central
                1742-4682
                2013
                18 September 2013
                : 10
                : 57
                Affiliations
                [1 ]Department of Biology, Faculty of Science, University Putra Malaysia, Serdang, Selangor, Malaysia
                [2 ]Department of Biostatistics and Epidemiology, School of Public Health, Kermanshah University of Medical Sciences, Kermanshah, Iran
                [3 ]Department of Mathematics, Faculty of Science, University Putra Malaysia, Serdang, , Selangor, Malaysia
                Article
                1742-4682-10-57
                10.1186/1742-4682-10-57
                3848855
                24044669
                6b0b8cf0-3ca0-4324-be9b-0276d2de4489
                Copyright © 2013 Salari et al.; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 18 December 2012
                : 4 September 2013
                Categories
                Commentary

                Quantitative & Systems biology
                acute coronary syndrome,artificial intelligence,clinical decision support systems,classification,diagnosis

                Comments

                Comment on this article