Effective Diagnosis of Coronary Artery Disease Using The Rotation Forest Ensemble Method View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2011-09-13

AUTHORS

Esra Mahsereci Karabulut, Turgay İbrikçi

ABSTRACT

Coronary Artery Disease is a common heart disease related to disorders effecting the heart and blood vessels. Since the disease is one of the leading causes of heart attacks and thus deaths, diagnosis of the disease in its early stages or in cases when patients do not show many of the symptoms yet has considerable importance. In the literature, studies based on computational methods have been proposed to diagnose the disease with readily available and easily collected patient data, and among these studies, the greatest accuracy reached is 89.01%. This paper presents a computational tool based on the Rotation Forest algorithm to effectively diagnose Coronary Artery Disease in order to support clinical decision-making processes. The proposed method utilizes Artificial Neural Networks with the Levenberg-Marquardt back propagation algorithm as base classifiers of the Rotation Forest ensemble method. In this scheme, 91.2% accuracy in diagnosing the disease is accomplished, which is, to the best of our knowledge, the best performance among the computational methods from the literature that use the same data. This paper also presents a comparison of the proposed method with some other classifiers in terms of diagnosis performance of Coronary Artery Disease. More... »

PAGES

3011-3018

References to SciGraph publications

  • 2006-01-01. Trade-Off Between Diversity and Accuracy in Ensemble Generation in MULTI-OBJECTIVE MACHINE LEARNING
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10916-011-9778-y

    DOI

    http://dx.doi.org/10.1007/s10916-011-9778-y

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1017826881

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/21912972


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Information and Computing Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Medical and Health Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Artificial Intelligence and Image Processing", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1102", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Cardiorespiratory Medicine and Haematology", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Age Factors", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Algorithms", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Cardiovascular Diseases", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Coronary Artery Disease", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Diagnosis, Computer-Assisted", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Humans", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Neural Networks, Computer", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Sex Factors", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Vocational School of Higher Education, University of Gaziantep, 27300, Gaziantep, Turkey", 
              "id": "http://www.grid.ac/institutes/grid.411549.c", 
              "name": [
                "Vocational School of Higher Education, University of Gaziantep, 27300, Gaziantep, Turkey"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Karabulut", 
            "givenName": "Esra Mahsereci", 
            "id": "sg:person.01112263703.05", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01112263703.05"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Electrical-Electronics Department, \u00c7ukurova University, 01330, Balcal\u0131, Adana, Turkey", 
              "id": "http://www.grid.ac/institutes/grid.98622.37", 
              "name": [
                "Electrical-Electronics Department, \u00c7ukurova University, 01330, Balcal\u0131, Adana, Turkey"
              ], 
              "type": "Organization"
            }, 
            "familyName": "\u0130brik\u00e7i", 
            "givenName": "Turgay", 
            "id": "sg:person.01313373253.97", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01313373253.97"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1007/3-540-33019-4_19", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1038431014", 
              "https://doi.org/10.1007/3-540-33019-4_19"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2011-09-13", 
        "datePublishedReg": "2011-09-13", 
        "description": "Coronary Artery Disease is a common heart disease related to disorders effecting the heart and blood vessels. Since the disease is one of the leading causes of heart attacks and thus deaths, diagnosis of the disease in its early stages or in cases when patients do not show many of the symptoms yet has considerable importance. In the literature, studies based on computational methods have been proposed to diagnose the disease with readily available and easily collected patient data, and among these studies, the greatest accuracy reached is 89.01%. This paper presents a computational tool based on the Rotation Forest algorithm to effectively diagnose Coronary Artery Disease in order to support clinical decision-making processes. The proposed method utilizes Artificial Neural Networks with the Levenberg-Marquardt back propagation algorithm as base classifiers of the Rotation Forest ensemble method. In this scheme, 91.2% accuracy in diagnosing the disease is accomplished, which is, to the best of our knowledge, the best performance among the computational methods from the literature that use the same data. This paper also presents a comparison of the proposed method with some other classifiers in terms of diagnosis performance of Coronary Artery Disease.", 
        "genre": "article", 
        "id": "sg:pub.10.1007/s10916-011-9778-y", 
        "isAccessibleForFree": false, 
        "isPartOf": [
          {
            "id": "sg:journal.1088158", 
            "issn": [
              "0148-5598", 
              "1573-689X"
            ], 
            "name": "Journal of Medical Systems", 
            "publisher": "Springer Nature", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "5", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "36"
          }
        ], 
        "keywords": [
          "coronary artery disease", 
          "artery disease", 
          "clinical decision-making process", 
          "common heart disease", 
          "heart disease", 
          "heart attack", 
          "disease", 
          "blood vessels", 
          "patient data", 
          "effective diagnosis", 
          "diagnosis", 
          "early stages", 
          "patients", 
          "symptoms", 
          "disorders", 
          "death", 
          "heart", 
          "cause", 
          "study", 
          "vessels", 
          "decision-making process", 
          "literature", 
          "data", 
          "cases", 
          "rotation forest ensemble method", 
          "stage", 
          "considerable importance", 
          "greater accuracy", 
          "method", 
          "knowledge", 
          "diagnosis performance", 
          "importance", 
          "comparison", 
          "attacks", 
          "tool", 
          "ensemble method", 
          "forest algorithm", 
          "artificial neural network", 
          "accuracy", 
          "rotation forest algorithm", 
          "same data", 
          "base classifiers", 
          "neural network", 
          "terms", 
          "propagation algorithm", 
          "computational methods", 
          "Levenberg-Marquardt", 
          "computational tools", 
          "order", 
          "classifier", 
          "better performance", 
          "process", 
          "algorithm", 
          "performance", 
          "network", 
          "scheme", 
          "paper"
        ], 
        "name": "Effective Diagnosis of Coronary Artery Disease Using The Rotation Forest Ensemble Method", 
        "pagination": "3011-3018", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1017826881"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s10916-011-9778-y"
            ]
          }, 
          {
            "name": "pubmed_id", 
            "type": "PropertyValue", 
            "value": [
              "21912972"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s10916-011-9778-y", 
          "https://app.dimensions.ai/details/publication/pub.1017826881"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2022-08-04T16:59", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-springernature-scigraph/baseset/20220804/entities/gbq_results/article/article_544.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://doi.org/10.1007/s10916-011-9778-y"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s10916-011-9778-y'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s10916-011-9778-y'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10916-011-9778-y'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10916-011-9778-y'


     

    This table displays all metadata directly associated to this object as RDF triples.

    172 TRIPLES      21 PREDICATES      93 URIs      82 LITERALS      15 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s10916-011-9778-y schema:about N1c70b3220280487792d1f1dedb7aa5c6
    2 N1ed82ffe841a44a1aaf086ee56ec909f
    3 N59cf4c306e8c403094777d2bd8a43b5d
    4 N73ee68a5e8c34dbd92b98d75115c33e0
    5 N8ee7c89e02ab464ebbe2f5dc56cf70b0
    6 Nbca4766d018d47f0a10e99a4bb1f0f88
    7 Ncfe8399c2d2548dba8afa4621da97299
    8 Nfb8247e0abe84b4194ae7ab8ac645bee
    9 anzsrc-for:08
    10 anzsrc-for:0801
    11 anzsrc-for:11
    12 anzsrc-for:1102
    13 schema:author Nc2b13d4bc01344d9a42f7008c0ad1dd2
    14 schema:citation sg:pub.10.1007/3-540-33019-4_19
    15 schema:datePublished 2011-09-13
    16 schema:datePublishedReg 2011-09-13
    17 schema:description Coronary Artery Disease is a common heart disease related to disorders effecting the heart and blood vessels. Since the disease is one of the leading causes of heart attacks and thus deaths, diagnosis of the disease in its early stages or in cases when patients do not show many of the symptoms yet has considerable importance. In the literature, studies based on computational methods have been proposed to diagnose the disease with readily available and easily collected patient data, and among these studies, the greatest accuracy reached is 89.01%. This paper presents a computational tool based on the Rotation Forest algorithm to effectively diagnose Coronary Artery Disease in order to support clinical decision-making processes. The proposed method utilizes Artificial Neural Networks with the Levenberg-Marquardt back propagation algorithm as base classifiers of the Rotation Forest ensemble method. In this scheme, 91.2% accuracy in diagnosing the disease is accomplished, which is, to the best of our knowledge, the best performance among the computational methods from the literature that use the same data. This paper also presents a comparison of the proposed method with some other classifiers in terms of diagnosis performance of Coronary Artery Disease.
    18 schema:genre article
    19 schema:isAccessibleForFree false
    20 schema:isPartOf N4711ab6738ae44aba12bdbe7ba19ca03
    21 N87fed6211c224f25b473469750123c9b
    22 sg:journal.1088158
    23 schema:keywords Levenberg-Marquardt
    24 accuracy
    25 algorithm
    26 artery disease
    27 artificial neural network
    28 attacks
    29 base classifiers
    30 better performance
    31 blood vessels
    32 cases
    33 cause
    34 classifier
    35 clinical decision-making process
    36 common heart disease
    37 comparison
    38 computational methods
    39 computational tools
    40 considerable importance
    41 coronary artery disease
    42 data
    43 death
    44 decision-making process
    45 diagnosis
    46 diagnosis performance
    47 disease
    48 disorders
    49 early stages
    50 effective diagnosis
    51 ensemble method
    52 forest algorithm
    53 greater accuracy
    54 heart
    55 heart attack
    56 heart disease
    57 importance
    58 knowledge
    59 literature
    60 method
    61 network
    62 neural network
    63 order
    64 paper
    65 patient data
    66 patients
    67 performance
    68 process
    69 propagation algorithm
    70 rotation forest algorithm
    71 rotation forest ensemble method
    72 same data
    73 scheme
    74 stage
    75 study
    76 symptoms
    77 terms
    78 tool
    79 vessels
    80 schema:name Effective Diagnosis of Coronary Artery Disease Using The Rotation Forest Ensemble Method
    81 schema:pagination 3011-3018
    82 schema:productId N1740dc7e631045c7a90c5e17a7ce822f
    83 N7aa49465f84b462d911fee67da335484
    84 Ne43405752f44465190b8c5a07e32e42a
    85 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017826881
    86 https://doi.org/10.1007/s10916-011-9778-y
    87 schema:sdDatePublished 2022-08-04T16:59
    88 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    89 schema:sdPublisher Na85e2f4e28a54d7ba92671963ed0c0a0
    90 schema:url https://doi.org/10.1007/s10916-011-9778-y
    91 sgo:license sg:explorer/license/
    92 sgo:sdDataset articles
    93 rdf:type schema:ScholarlyArticle
    94 N1740dc7e631045c7a90c5e17a7ce822f schema:name dimensions_id
    95 schema:value pub.1017826881
    96 rdf:type schema:PropertyValue
    97 N1c70b3220280487792d1f1dedb7aa5c6 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    98 schema:name Sex Factors
    99 rdf:type schema:DefinedTerm
    100 N1ed82ffe841a44a1aaf086ee56ec909f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    101 schema:name Coronary Artery Disease
    102 rdf:type schema:DefinedTerm
    103 N4711ab6738ae44aba12bdbe7ba19ca03 schema:issueNumber 5
    104 rdf:type schema:PublicationIssue
    105 N59cf4c306e8c403094777d2bd8a43b5d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    106 schema:name Neural Networks, Computer
    107 rdf:type schema:DefinedTerm
    108 N73ee68a5e8c34dbd92b98d75115c33e0 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    109 schema:name Humans
    110 rdf:type schema:DefinedTerm
    111 N7aa49465f84b462d911fee67da335484 schema:name doi
    112 schema:value 10.1007/s10916-011-9778-y
    113 rdf:type schema:PropertyValue
    114 N87fed6211c224f25b473469750123c9b schema:volumeNumber 36
    115 rdf:type schema:PublicationVolume
    116 N8ee7c89e02ab464ebbe2f5dc56cf70b0 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    117 schema:name Diagnosis, Computer-Assisted
    118 rdf:type schema:DefinedTerm
    119 Na85e2f4e28a54d7ba92671963ed0c0a0 schema:name Springer Nature - SN SciGraph project
    120 rdf:type schema:Organization
    121 Nbca4766d018d47f0a10e99a4bb1f0f88 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    122 schema:name Age Factors
    123 rdf:type schema:DefinedTerm
    124 Nc2b13d4bc01344d9a42f7008c0ad1dd2 rdf:first sg:person.01112263703.05
    125 rdf:rest Ne805f0374de5434bb4c14e5e10f0026d
    126 Ncfe8399c2d2548dba8afa4621da97299 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    127 schema:name Algorithms
    128 rdf:type schema:DefinedTerm
    129 Ne43405752f44465190b8c5a07e32e42a schema:name pubmed_id
    130 schema:value 21912972
    131 rdf:type schema:PropertyValue
    132 Ne805f0374de5434bb4c14e5e10f0026d rdf:first sg:person.01313373253.97
    133 rdf:rest rdf:nil
    134 Nfb8247e0abe84b4194ae7ab8ac645bee schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    135 schema:name Cardiovascular Diseases
    136 rdf:type schema:DefinedTerm
    137 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    138 schema:name Information and Computing Sciences
    139 rdf:type schema:DefinedTerm
    140 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
    141 schema:name Artificial Intelligence and Image Processing
    142 rdf:type schema:DefinedTerm
    143 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
    144 schema:name Medical and Health Sciences
    145 rdf:type schema:DefinedTerm
    146 anzsrc-for:1102 schema:inDefinedTermSet anzsrc-for:
    147 schema:name Cardiorespiratory Medicine and Haematology
    148 rdf:type schema:DefinedTerm
    149 sg:journal.1088158 schema:issn 0148-5598
    150 1573-689X
    151 schema:name Journal of Medical Systems
    152 schema:publisher Springer Nature
    153 rdf:type schema:Periodical
    154 sg:person.01112263703.05 schema:affiliation grid-institutes:grid.411549.c
    155 schema:familyName Karabulut
    156 schema:givenName Esra Mahsereci
    157 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01112263703.05
    158 rdf:type schema:Person
    159 sg:person.01313373253.97 schema:affiliation grid-institutes:grid.98622.37
    160 schema:familyName İbrikçi
    161 schema:givenName Turgay
    162 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01313373253.97
    163 rdf:type schema:Person
    164 sg:pub.10.1007/3-540-33019-4_19 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038431014
    165 https://doi.org/10.1007/3-540-33019-4_19
    166 rdf:type schema:CreativeWork
    167 grid-institutes:grid.411549.c schema:alternateName Vocational School of Higher Education, University of Gaziantep, 27300, Gaziantep, Turkey
    168 schema:name Vocational School of Higher Education, University of Gaziantep, 27300, Gaziantep, Turkey
    169 rdf:type schema:Organization
    170 grid-institutes:grid.98622.37 schema:alternateName Electrical-Electronics Department, Çukurova University, 01330, Balcalı, Adana, Turkey
    171 schema:name Electrical-Electronics Department, Çukurova University, 01330, Balcalı, Adana, Turkey
    172 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...