Liver Cancer Analysis Using Supervised Machine Learning Classifiers
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Liver Cancer Analysis Using Supervised Machine Learning Classifiers (2019)
~EN PB NWISBN: 9783330335912 bzw. 3330335912, vermutlich in Englisch, LAP LAMBERT Academic Publishing, Taschenbuch, neu.
Lieferung aus: Deutschland, Lieferbar in 2 - 3 Tage.
Artificial intelligence in medical diagnosis The research work has a notable social impact as it facilitates liver cancer diagnosis on the basis of statistical approaches and experimental performance of machine learning classifiers on ILPD(Indian Liver Patient Dataset) and BUPA liver datasets. The work embodies certain discovered facts. The liver cancer diagnosis can be governed by concept learning, artificial neural modeling, geometric distribution and Cobb-Douglas model. The augmentation or expansion of features indicating liver cancer growth can be quantified and realized based on Markov property based state transition. Liver cancer detection can also be analyzed based upon the fundamental principle of information gain. The realibility and mean time to failure of liver cancer testing system can be carried out in the light of parallel system configuration. The factor leading to liver cancer can be sensed on the basis of weighted majority algorithms.The present objective is also to propose a method using supervised machine learning that can help the physician for accurate diagnosis of liver cancer. For experimental analysis two liver cancer datasets and six diverse classifiers in machine learning have been used. 22.04.2019, Taschenbuch.
Artificial intelligence in medical diagnosis The research work has a notable social impact as it facilitates liver cancer diagnosis on the basis of statistical approaches and experimental performance of machine learning classifiers on ILPD(Indian Liver Patient Dataset) and BUPA liver datasets. The work embodies certain discovered facts. The liver cancer diagnosis can be governed by concept learning, artificial neural modeling, geometric distribution and Cobb-Douglas model. The augmentation or expansion of features indicating liver cancer growth can be quantified and realized based on Markov property based state transition. Liver cancer detection can also be analyzed based upon the fundamental principle of information gain. The realibility and mean time to failure of liver cancer testing system can be carried out in the light of parallel system configuration. The factor leading to liver cancer can be sensed on the basis of weighted majority algorithms.The present objective is also to propose a method using supervised machine learning that can help the physician for accurate diagnosis of liver cancer. For experimental analysis two liver cancer datasets and six diverse classifiers in machine learning have been used. 22.04.2019, Taschenbuch.
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Liver Cancer Analysis Using Supervised Machine Learning Classifiers - Artificial intelligence in medical diagnosis
~EN PB NWISBN: 9783330335912 bzw. 3330335912, vermutlich in Englisch, LAP Lambert Academic Publishing, Taschenbuch, neu.
Lieferung aus: Deutschland, Versandkostenfrei.
Liver Cancer Analysis Using Supervised Machine Learning Classifiers: The research work has a notable social impact as it facilitates liver cancer diagnosis on the basis of statistical approaches and experimental performance of machine learning classifiers on ILPD(Indian Liver Patient Dataset) and BUPA liver datasets. The work embodies certain discovered facts. The liver cancer diagnosis can be governed by concept learning, artificial neural modeling, geometric distribution and Cobb-Douglas model. The augmentation or expansion of features indicating liver cancer growth can be quantified and realized based on Markov property based state transition. Liver cancer detection can also be analyzed based upon the fundamental principle of information gain. The realibility and mean time to failure of liver cancer testing system can be carried out in the light of parallel system configuration. The factor leading to liver cancer can be sensed on the basis of weighted majority algorithms.The present objective is also to propose a method using supervised machine learning that can help the physician for accurate diagnosis of liver cancer. For experimental analysis two liver cancer datasets and six diverse classifiers in machine learning have been used. Englisch, Taschenbuch.
Liver Cancer Analysis Using Supervised Machine Learning Classifiers: The research work has a notable social impact as it facilitates liver cancer diagnosis on the basis of statistical approaches and experimental performance of machine learning classifiers on ILPD(Indian Liver Patient Dataset) and BUPA liver datasets. The work embodies certain discovered facts. The liver cancer diagnosis can be governed by concept learning, artificial neural modeling, geometric distribution and Cobb-Douglas model. The augmentation or expansion of features indicating liver cancer growth can be quantified and realized based on Markov property based state transition. Liver cancer detection can also be analyzed based upon the fundamental principle of information gain. The realibility and mean time to failure of liver cancer testing system can be carried out in the light of parallel system configuration. The factor leading to liver cancer can be sensed on the basis of weighted majority algorithms.The present objective is also to propose a method using supervised machine learning that can help the physician for accurate diagnosis of liver cancer. For experimental analysis two liver cancer datasets and six diverse classifiers in machine learning have been used. Englisch, Taschenbuch.
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Liver Cancer Analysis Using Supe
~EN PB NWISBN: 9783330335912 bzw. 3330335912, vermutlich in Englisch, Taschenbuch, neu.
Lieferung aus: Deutschland, Next Day, Versandkostenfrei.
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