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The diagnosis of heart disease is a significant and tedious task in medicine. The healthcare industry gathers enormous amounts of heart disease data that regrettably, are not “mined” to determine concealed information for effective decision making by healthcare practitioners. The term Heart disease encompasses the diverse diseases that affect the heart. Cardiomyopathy and Cardiovascular disease are some categories of heart diseases. The reduction of blood and oxygen supply to the heart leads to heart disease. In this paper the data classification is based on supervised machine learning algorithms which result in accuracy, time taken to build the algorithm. Tanagra tool is used to classify the data and the data is evaluated using 10-fold cross validation and the results are compared.
Asha Rajkumar M.phil (Computer Science). 1970. \u201cDiagonsis of Heaer Disease using Datamining Algorithm\u201d. Unknown Journal GJCST Volume 10 (GJCST Volume 10 Issue 10): .
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Total Score: 102
Country: India
Subject: Uncategorized
Authors: Asha Rajkumar M.phil (Computer Science), G.Sophia Reena (HOD of BCA Department) (PhD/Dr. count: 0)
View Count (all-time): 115
Total Views (Real + Logic): 20751
Total Downloads (simulated): 10930
Publish Date: 1970 01, Thu
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The diagnosis of heart disease is a significant and tedious task in medicine. The healthcare industry gathers enormous amounts of heart disease data that regrettably, are not “mined” to determine concealed information for effective decision making by healthcare practitioners. The term Heart disease encompasses the diverse diseases that affect the heart. Cardiomyopathy and Cardiovascular disease are some categories of heart diseases. The reduction of blood and oxygen supply to the heart leads to heart disease. In this paper the data classification is based on supervised machine learning algorithms which result in accuracy, time taken to build the algorithm. Tanagra tool is used to classify the data and the data is evaluated using 10-fold cross validation and the results are compared.
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