Decision Trees and Hybrid Approaches: Improved Classification Of Medical Data Using Decision Trees and Hybrid Approaches

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9783639662627: Decision Trees and Hybrid Approaches: Improved Classification Of Medical Data Using Decision Trees and Hybrid Approaches
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Accuracy and efficiency are very much required in medical diagnosis though it is an important at the same time complicated task. The automated medical diagnosis system would be highly beneficial.This automation removes unwanted biases, errors and costs which affects the quality of clinical diagnosis. Data mining techniques especially Decision Trees play an efficient role in the classification of medical data. To know the best decision tree classifier for medical data sets various frequently used decision tree algorithms are compared based on their classification accuracy. As per the statistics of National Cancer Institute Breast cancer is a leading cause of death among females in economically developing countries and a second cause in developed countries.Early detection of breast cancer will reduce the death rate.In order to extract the most relevant features from the data sets various Feature Selection methods and Hybrid Approaches with Ensemble techniques ie., Bagging and Boosting with decision tree classifier on breast cancer data sets are studied. And a new hybrid algorithm is proposed with cascading Feature selection, Clustering and Classification to enhance the accuracy.

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D.Lavanya M.Sc,M.Tech,Ph.D working currently as Professor in the Department of Computer science and Engineering , Siddartha Educational Academy Group of Institutions, Tirupati, Andhra Pradesh, India. Her research interests include Data Mining, Database Systems, Soft Computing and the teaching experience is 13 years.

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Book Description SPS Sep 2014, 2014. Taschenbuch. Condition: Neu. Neuware - Accuracy and efficiency are very much required in medical diagnosis though it is an important at the same time complicated task. The automated medical diagnosis system would be highly beneficial.This automation removes unwanted biases, errors and costs which affects the quality of clinical diagnosis. Data mining techniques especially Decision Trees play an efficient role in the classification of medical data. To know the best decision tree classifier for medical data sets various frequently used decision tree algorithms are compared based on their classification accuracy. As per the statistics of National Cancer Institute Breast cancer is a leading cause of death among females in economically developing countries and a second cause in developed countries.Early detection of breast cancer will reduce the death rate.In order to extract the most relevant features from the data sets various Feature Selection methods and Hybrid Approaches with Ensemble techniques ie., Bagging and Boosting with decision tree classifier on breast cancer data sets are studied. And a new hybrid algorithm is proposed with cascading Feature selection, Clustering and Classification to enhance the accuracy. 96 pp. Englisch. Seller Inventory # 9783639662627

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Book Description SPS Sep 2014, 2014. Taschenbuch. Condition: Neu. Neuware - Accuracy and efficiency are very much required in medical diagnosis though it is an important at the same time complicated task. The automated medical diagnosis system would be highly beneficial.This automation removes unwanted biases, errors and costs which affects the quality of clinical diagnosis. Data mining techniques especially Decision Trees play an efficient role in the classification of medical data. To know the best decision tree classifier for medical data sets various frequently used decision tree algorithms are compared based on their classification accuracy. As per the statistics of National Cancer Institute Breast cancer is a leading cause of death among females in economically developing countries and a second cause in developed countries.Early detection of breast cancer will reduce the death rate.In order to extract the most relevant features from the data sets various Feature Selection methods and Hybrid Approaches with Ensemble techniques ie., Bagging and Boosting with decision tree classifier on breast cancer data sets are studied. And a new hybrid algorithm is proposed with cascading Feature selection, Clustering and Classification to enhance the accuracy. 96 pp. Englisch. Seller Inventory # 9783639662627

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Book Description SPS Sep 2014, 2014. Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Neuware - Accuracy and efficiency are very much required in medical diagnosis though it is an important at the same time complicated task. The automated medical diagnosis system would be highly beneficial.This automation removes unwanted biases, errors and costs which affects the quality of clinical diagnosis. Data mining techniques especially Decision Trees play an efficient role in the classification of medical data. To know the best decision tree classifier for medical data sets various frequently used decision tree algorithms are compared based on their classification accuracy. As per the statistics of National Cancer Institute Breast cancer is a leading cause of death among females in economically developing countries and a second cause in developed countries.Early detection of breast cancer will reduce the death rate.In order to extract the most relevant features from the data sets various Feature Selection methods and Hybrid Approaches with Ensemble techniques ie., Bagging and Boosting with decision tree classifier on breast cancer data sets are studied. And a new hybrid algorithm is proposed with cascading Feature selection, Clustering and Classification to enhance the accuracy. 96 pp. Englisch. Seller Inventory # 9783639662627

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Book Description Scholars Press, United States, 2014. Paperback. Condition: New. Language: English . Brand New Book ***** Print on Demand *****.Accuracy and efficiency are very much required in medical diagnosis though it is an important at the same time complicated task. The automated medical diagnosis system would be highly beneficial.This automation removes unwanted biases, errors and costs which affects the quality of clinical diagnosis. Data mining techniques especially Decision Trees play an efficient role in the classification of medical data. To know the best decision tree classifier for medical data sets various frequently used decision tree algorithms are compared based on their classification accuracy. As per the statistics of National Cancer Institute Breast cancer is a leading cause of death among females in economically developing countries and a second cause in developed countries.Early detection of breast cancer will reduce the death rate.In order to extract the most relevant features from the data sets various Feature Selection methods and Hybrid Approaches with Ensemble techniques ie., Bagging and Boosting with decision tree classifier on breast cancer data sets are studied. And a new hybrid algorithm is proposed with cascading Feature selection, Clustering and Classification to enhance the accuracy. Seller Inventory # AAV9783639662627

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Book Description Scholars' Press. Paperback. Condition: New. 96 pages. Dimensions: 8.7in. x 5.9in. x 0.2in.Accuracy and efficiency are very much required in medical diagnosis though it is an important at the same time complicated task. The automated medical diagnosis system would be highly beneficial. This automation removes unwanted biases, errors and costs which affects the quality of clinical diagnosis. Data mining techniques especially Decision Trees play an efficient role in the classification of medical data. To know the best decision tree classifier for medical data sets various frequently used decision tree algorithms are compared based on their classification accuracy. As per the statistics of National Cancer Institute Breast cancer is a leading cause of death among females in economically developing countries and a second cause in developed countries. Early detection of breast cancer will reduce the death rate. In order to extract the most relevant features from the data sets various Feature Selection methods and Hybrid Approaches with Ensemble techniques ie. , Bagging and Boosting with decision tree classifier on breast cancer data sets are studied. And a new hybrid algorithm is proposed with cascading Feature selection, Clustering and Classification to enhance the accuracy. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN. Paperback. Seller Inventory # 9783639662627

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