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      Predicting coronary artery disease: a comparison between two data mining algorithms

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          Abstract

          Background

          Cardiovascular diseases (CADs) are the first leading cause of death across the world. World Health Organization has estimated that morality rate caused by heart diseases will mount to 23 million cases by 2030. Hence, the use of data mining algorithms could be useful in predicting coronary artery diseases. Therefore, the present study aimed to compare the positive predictive value (PPV) of CAD using artificial neural network (ANN) and SVM algorithms and their distinction in terms of predicting CAD in the selected hospitals.

          Methods

          The present study was conducted by using data mining techniques. The research sample was the medical records of the patients with coronary artery disease who were hospitalized in three hospitals affiliated to AJA University of Medical Sciences between March 2016 and March 2017 ( n = 1324). The dataset and the predicting variables used in this study was the same for both data mining techniques. Totally, 25 variables affecting CAD were selected and related data were extracted. After normalizing and cleaning the data, they were entered into SPSS (V23.0) and Excel 2013. Then, R 3.3.2 was used for statistical computing.

          Results

          The SVM model had lower MAPE (112.03), higher Hosmer-Lemeshow test’s result (16.71), and higher sensitivity (92.23). Moreover, variables affecting CAD (74.42) yielded better goodness of fit in SVM model and provided more accurate result than the ANN model. On the other hand, since the area under the receiver operating characteristic (ROC) curve in the SVM algorithm was more than this area in ANN model, it could be concluded that SVM model had higher accuracy than the ANN model.

          Conclusion

          According to the results, the SVM algorithm presented higher accuracy and better performance than the ANN model and was characterized with higher power and sensitivity. Overall, it provided a better classification for the prediction of CAD. The use of other data mining algorithms are suggested to improve the positive predictive value of the disease prediction.

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          Most cited references39

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          Epidemiology of coronary heart disease and acute coronary syndrome.

          The aim of this review is to summarize the incidence, prevalence, trend in mortality, and general prognosis of coronary heart disease (CHD) and a related condition, acute coronary syndrome (ACS). Although CHD mortality has gradually declined over the last decades in western countries, this condition still causes about one-third of all deaths in people older than 35 years. This evidence, along with the fact that mortality from CHD is expected to continue increasing in developing countries, illustrates the need for implementing effective primary prevention approaches worldwide and identifying risk groups and areas for possible improvement.
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            Association between coronary vascular dysfunction and cardiac mortality in patients with and without diabetes mellitus.

            Diabetes mellitus increases the risk of adverse cardiac outcomes and is considered a coronary artery disease (CAD) equivalent. We examined whether coronary vascular dysfunction, an early manifestation of CAD, accounts for increased risk among diabetics compared with nondiabetics. A total of 2783 consecutive patients (1172 diabetics and 1611 nondiabetics) underwent quantification of coronary flow reserve (CFR; CFR=stress divided by rest myocardial blood flow) by positron emission tomography and were followed up for a median of 1.4 years (quartile 1-3, 0.7-3.2 years). The primary end point was cardiac death. Impaired CFR (below the median) was associated with an adjusted 3.2- and 4.9-fold increase in the rate of cardiac death for diabetics and nondiabetics, respectively (P=0.0004). Addition of CFR to clinical and imaging risk models improved risk discrimination for both diabetics and nondiabetics (c index, 0.77-0.79, P=0.04; 0.82-0.85, P=0.03, respectively). Diabetic patients without known CAD with impaired CFR experienced a rate of cardiac death comparable to that for nondiabetic patients with known CAD (2.8%/y versus 2.0%/y; P=0.33). Conversely, diabetics without known CAD and preserved CFR had very low annualized cardiac mortality, which was similar to patients without known CAD or diabetes mellitus and normal stress perfusion and systolic function (0.3%/y versus 0.5%/y; P=0.65). Coronary vasodilator dysfunction is a powerful, independent correlate of cardiac mortality among both diabetics and nondiabetics and provides meaningful incremental risk stratification. Among diabetic patients without CAD, those with impaired CFR have event rates comparable to those of patients with prior CAD, whereas those with preserved CFR have event rates comparable to those of nondiabetics.
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              Artificial neural networks in medical diagnosis

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                Author and article information

                Contributors
                ayatollahi.h@iums.ac.ir
                gholamhosseini.l@tak.iums.ac.ir
                salehi.m@iums.ac.ir
                Journal
                BMC Public Health
                BMC Public Health
                BMC Public Health
                BioMed Central (London )
                1471-2458
                29 April 2019
                29 April 2019
                2019
                : 19
                : 448
                Affiliations
                [1 ]ISNI 0000 0004 4911 7066, GRID grid.411746.1, Health Management and Economics Research Center, , Iran University of Medical Sciences, ; Tehran, Iran
                [2 ]ISNI 0000 0004 4911 7066, GRID grid.411746.1, Department of Health Information Management, School of Health Management and Information Sciences, , Iran University of Medical Sciences, ; Tehran, Iran
                [3 ]ISNI 0000 0000 9286 0323, GRID grid.411259.a, School of Paramedical Sciences, AJA University of Medical Sciences, ; Tehran, Iran
                [4 ]ISNI 0000 0004 4911 7066, GRID grid.411746.1, Department of Biostatistics, School of Public Health, , Iran University of Medical Sciences, ; Tehran, Iran
                Author information
                http://orcid.org/0000-0002-0502-6096
                Article
                6721
                10.1186/s12889-019-6721-5
                6489351
                31035958
                ed1111a4-76d5-4bf6-8858-4dd07d5baef3
                © The Author(s). 2019

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 29 September 2018
                : 28 March 2019
                Funding
                Funded by: AJA University of Medical Sciences
                Award ID: 594273
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2019

                Public health
                coronary artery disease (cad),data mining algorithms,artificial neural network (ann),support vector machine (svm)

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