Early and accurate detection and diagnosis of heart disease using intelligent computational model
Heart disease is a fatal human disease, rapidly increases globally in both developed and undeveloped countries and consequently, causes death. Normally, in this disease, the heart fails to supply a sufficient amount of blood to other parts of the body in order to accomplish their normal functionalit...
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description | Heart disease is a fatal human disease, rapidly increases globally in both developed and undeveloped countries and consequently, causes death. Normally, in this disease, the heart fails to supply a sufficient amount of blood to other parts of the body in order to accomplish their normal functionalities. Early and on-time diagnosing of this problem is very essential for preventing patients from more damage and saving their lives. Among the conventional invasive-based techniques, angiography is considered to be the most well-known technique for diagnosing heart problems but it has some limitations. On the other hand, the non-invasive based methods, like intelligent learning-based computational techniques are found more upright and effectual for the heart disease diagnosis. Here, an intelligent computational predictive system is introduced for the identification and diagnosis of cardiac disease. In this study, various machine learning classification algorithms are investigated. In order to remove irrelevant and noisy data from extracted feature space, four distinct feature selection algorithms are applied and the results of each feature selection algorithm along with classifiers are analyzed. Several performance metrics namely: accuracy, sensitivity, specificity, AUC, F1-score, MCC, and ROC curve are used to observe the effectiveness and strength of the developed model. The classification rates of the developed system are examined on both full and optimal feature spaces, consequently, the performance of the developed model is boosted in case of high variated optimal feature space. In addition, P-value and Chi-square are also computed for the ET classifier along with each feature selection technique. It is anticipated that the proposed system will be useful and helpful for the physician to diagnose heart disease accurately and effectively. |
doi_str_mv | 10.1038/s41598-020-76635-9 |
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It is anticipated that the proposed system will be useful and helpful for the physician to diagnose heart disease accurately and effectively.</description><identifier>ISSN: 2045-2322</identifier><identifier>EISSN: 2045-2322</identifier><identifier>DOI: 10.1038/s41598-020-76635-9</identifier><identifier>PMID: 33184369</identifier><language>eng</language><publisher>London: Nature Publishing Group UK</publisher><subject>631/114 ; 692/699 ; 692/699/75 ; 692/699/75/230 ; 692/700 ; Adult ; Aged ; Aged, 80 and over ; Algorithms ; Angiography ; Artificial Intelligence ; Cardiovascular disease ; Cardiovascular diseases ; Classification ; Computational Biology - methods ; Computer applications ; Coronary artery disease ; Diagnosis ; Feature selection ; Female ; Follow-Up Studies ; Heart ; Heart diseases ; Heart Diseases - diagnosis ; Humanities and Social Sciences ; Humans ; Learning algorithms ; Machine Learning ; Male ; Middle Aged ; Models, Statistical ; multidisciplinary ; Prognosis ; ROC Curve ; Science ; Science (multidisciplinary)</subject><ispartof>Scientific reports, 2020-11, Vol.10 (1), p.19747-19747, Article 19747</ispartof><rights>The Author(s) 2020</rights><rights>The Author(s) 2020. 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Normally, in this disease, the heart fails to supply a sufficient amount of blood to other parts of the body in order to accomplish their normal functionalities. Early and on-time diagnosing of this problem is very essential for preventing patients from more damage and saving their lives. Among the conventional invasive-based techniques, angiography is considered to be the most well-known technique for diagnosing heart problems but it has some limitations. On the other hand, the non-invasive based methods, like intelligent learning-based computational techniques are found more upright and effectual for the heart disease diagnosis. Here, an intelligent computational predictive system is introduced for the identification and diagnosis of cardiac disease. In this study, various machine learning classification algorithms are investigated. In order to remove irrelevant and noisy data from extracted feature space, four distinct feature selection algorithms are applied and the results of each feature selection algorithm along with classifiers are analyzed. Several performance metrics namely: accuracy, sensitivity, specificity, AUC, F1-score, MCC, and ROC curve are used to observe the effectiveness and strength of the developed model. The classification rates of the developed system are examined on both full and optimal feature spaces, consequently, the performance of the developed model is boosted in case of high variated optimal feature space. In addition, P-value and Chi-square are also computed for the ET classifier along with each feature selection technique. It is anticipated that the proposed system will be useful and helpful for the physician to diagnose heart disease accurately and effectively.</abstract><cop>London</cop><pub>Nature Publishing Group UK</pub><pmid>33184369</pmid><doi>10.1038/s41598-020-76635-9</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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subjects | 631/114 692/699 692/699/75 692/699/75/230 692/700 Adult Aged Aged, 80 and over Algorithms Angiography Artificial Intelligence Cardiovascular disease Cardiovascular diseases Classification Computational Biology - methods Computer applications Coronary artery disease Diagnosis Feature selection Female Follow-Up Studies Heart Heart diseases Heart Diseases - diagnosis Humanities and Social Sciences Humans Learning algorithms Machine Learning Male Middle Aged Models, Statistical multidisciplinary Prognosis ROC Curve Science Science (multidisciplinary) |
title | Early and accurate detection and diagnosis of heart disease using intelligent computational model |
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