Isbn: 9783330335912 - liver cancer analysis using supervised machine learning classifiers: artificial intelligence in medical diagnosis (4 resultados)

Paperback. Condición: Brand New. 176 pages. 8.66x5.91x0.40 inches. In Stock.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 176 pp. Englisch.…

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Tiwari ManishMr. Manish Tiwari is Assistant Professor, CSE Dept, GITS Udaipur - Prof. P. Chakrabarti is Dean(R&D) and Senior Professor, Dept of CSE, ITM Universe Vadodara - Dr. Tulika Chakrabarti is Assistant Professor (Senior Grade. …

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 176 pp. Englisch.…