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Medical decision support system (MDSS) are now being used in many health care institutions across the glove, these institutions have large amount of medical data stored in different format and may contain relevant data that are hidden. The use of data mining is to extract hidden knowledge from a relevant data, that is why the main aim of this book is to show how data mining methods can be applied in medical decision support system and also to design a web based expert system that can predict heart condition using neural network. The design of the system is based on VA Medical center long beach database and collected from the UCI machine learning repository. After analyzing several medical decision support systems in the relevant literature, three algorithms have been identified: multilayer perceptron, decision tree and Naïve Bayes. These algorithms are tested under different configuration in order to find the best on the two medical dataset. Thereafter, a comparison was made with respect to their performance based on some set of performance metrics. The analysis was done using WEKA on the two medical dataset which are diabetes and heart diseases database.
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Medical decision support system (MDSS) are now being used in many health care institutions across the glove, these institutions have large amount of medical data stored in different format and may contain relevant data that are hidden. The use of data mining is to extract hidden knowledge from a relevant data, that is why the main aim of this book is to show how data mining methods can be applied in medical decision support system and also to design a web based expert system that can predict heart condition using neural network. The design of the system is based on VA Medical center long beach database and collected from the UCI machine learning repository. After analyzing several medical decision support systems in the relevant literature, three algorithms have been identified: multilayer perceptron, decision tree and Naïve Bayes. These algorithms are tested under different configuration in order to find the best on the two medical dataset. Thereafter, a comparison was made with respect to their performance based on some set of performance metrics. The analysis was done using WEKA on the two medical dataset which are diabetes and heart diseases database.
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Application of data mining in medical decision support systems
Verlag:
LAP LAMBERT Academic Publishing, 2019
ISBN 10: 6139920140
ISBN 13: 9786139920143
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Mahamud Habib ShariffI was born Feb 1973, I had a BTech and MSc in computer science from University of East London. My area of interest is data mining and information systems, currently doing my Phd research on information systems. . Bestandsnummer des Verkäufers 385877325
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Application of data mining in medical decision support systems
Verlag:
LAP LAMBERT Academic Publishing Jul 2019, 2019
ISBN 10: 6139920140
ISBN 13: 9786139920143
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Taschenbuch. Zustand: Neu. Neuware -Medical decision support system (MDSS) are now being used in many health care institutions across the glove, these institutions have large amount of medical data stored in different format and may contain relevant data that are hidden. The use of data mining is to extract hidden knowledge from a relevant data, that is why the main aim of this book is to show how data mining methods can be applied in medical decision support system and also to design a web based expert system that can predict heart condition using neural network. The design of the system is based on VA Medical center long beach database and collected from the UCI machine learning repository. After analyzing several medical decision support systems in the relevant literature, three algorithms have been identified: multilayer perceptron, decision tree and Naïve Bayes. These algorithms are tested under different configuration in order to find the best on the two medical dataset. Thereafter, a comparison was made with respect to their performance based on some set of performance metrics. The analysis was done using WEKA on the two medical dataset which are diabetes and heart diseases database.Books on Demand GmbH, Überseering 33, 22297 Hamburg 84 pp. Englisch. Bestandsnummer des Verkäufers 9786139920143
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Application of data mining in medical decision support systems
Verlag:
LAP LAMBERT Academic Publishing Jul 2019, 2019
ISBN 10: 6139920140
ISBN 13: 9786139920143
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Medical decision support system (MDSS) are now being used in many health care institutions across the glove, these institutions have large amount of medical data stored in different format and may contain relevant data that are hidden. The use of data mining is to extract hidden knowledge from a relevant data, that is why the main aim of this book is to show how data mining methods can be applied in medical decision support system and also to design a web based expert system that can predict heart condition using neural network. The design of the system is based on VA Medical center long beach database and collected from the UCI machine learning repository. After analyzing several medical decision support systems in the relevant literature, three algorithms have been identified: multilayer perceptron, decision tree and Naïve Bayes. These algorithms are tested under different configuration in order to find the best on the two medical dataset. Thereafter, a comparison was made with respect to their performance based on some set of performance metrics. The analysis was done using WEKA on the two medical dataset which are diabetes and heart diseases database. 84 pp. Englisch. Bestandsnummer des Verkäufers 9786139920143
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Application of data mining in medical decision support systems
Verlag:
LAP LAMBERT Academic Publishing, 2019
ISBN 10: 6139920140
ISBN 13: 9786139920143
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Medical decision support system (MDSS) are now being used in many health care institutions across the glove, these institutions have large amount of medical data stored in different format and may contain relevant data that are hidden. The use of data mining is to extract hidden knowledge from a relevant data, that is why the main aim of this book is to show how data mining methods can be applied in medical decision support system and also to design a web based expert system that can predict heart condition using neural network. The design of the system is based on VA Medical center long beach database and collected from the UCI machine learning repository. After analyzing several medical decision support systems in the relevant literature, three algorithms have been identified: multilayer perceptron, decision tree and Naïve Bayes. These algorithms are tested under different configuration in order to find the best on the two medical dataset. Thereafter, a comparison was made with respect to their performance based on some set of performance metrics. The analysis was done using WEKA on the two medical dataset which are diabetes and heart diseases database. Bestandsnummer des Verkäufers 9786139920143
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Application of data mining in medical decision support systems
Verlag:
LAP LAMBERT Academic Publishing, 2019
ISBN 10: 6139920140
ISBN 13: 9786139920143
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Application of data mining in medical decision support systems
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ISBN 10: 6139920140
ISBN 13: 9786139920143
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Application of data mining in medical decision support systems
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ISBN 13: 9786139920143
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Application of data mining in medical decision support systems
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ISBN 13: 9786139920143
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