9786138975007 - Prediction of Liver Cirrhosis Using Machine Learning: DE von Gadicha, Dr. Ajay; Gadicha, Dr. Vijay (8 Ergebnisse)

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Taschenbuch. Zustand: Neu. Prediction of Liver Cirrhosis Using Machine Learning | Ajay Gadicha (u. a.) | Taschenbuch | Englisch | 2022 | Scholars' Press | EAN 9786138975007 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The death rate in India is high due to Liver Cirrhosis as a result of a bad lifestyle, storage of food, uncontrolled blood sugar, obesity, smoking, consumption of alcohol, and inhaling of harmful gases. Earlier detection can reduce death rates and it also helps the doctors to give the proper treatment to the patients. The liver Cirrhosis datasets are analyzed by using Machine learning algorithms for accurate disease diagnosis. This paper proposed the four machine learning models such as SVM, Random Forest, Decision Tree, and Naive Bayes for analysis and prediction of liver cirrhosis. This work gathers 200 pictures of two separate classes, i.e., healthy liver, and unhealthy liver, using an image source. This image source is MedPix1, a free open-access archive of digital photographs that medical schools, medical practitioners, and academics can use. The methodology actually has a good result for the various classifiers but still can be improved. It is found that the Support vector machine and random forest classifiers give a 100% classification accuracy score followed by decision trees which are 84.61. The results achieved are relatively good when compared. 56 pp. Englisch.

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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. The death rate in India is high due to Liver Cirrhosis as a result of a bad lifestyle, storage of food, uncontrolled blood sugar, obesity, smoking, consumption of alcohol, and inhaling of harmful gases. Earlier detection can reduce death rates and it also h.

Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The death rate in India is high due to Liver Cirrhosis as a result of a bad lifestyle, storage of food, uncontrolled blood sugar, obesity, smoking, consumption of alcohol, and inhaling of harmful gases. Earlier detection can reduce death rates and it also helps the doctors to give the proper treatment to the patients. The liver Cirrhosis datasets are analyzed by using Machine learning algorithms for accurate disease diagnosis. This paper proposed the four machine learning models such as SVM, Random Forest, Decision Tree, and Naive Bayes for analysis and prediction of liver cirrhosis. This work gathers 200 pictures of two separate classes, i.e., healthy liver, and unhealthy liver, using an image source. This image source is MedPix1, a free open-access archive of digital photographs that medical schools, medical practitioners, and academics can use. The methodology actually has a good result for the various classifiers but still can be improved. It is found that the Support vector machine and random forest classifiers give a 100% classification accuracy score followed by decision trees which are 84.61. The results achieved are relatively good when compared.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 56 pp. Englisch.

Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The death rate in India is high due to Liver Cirrhosis as a result of a bad lifestyle, storage of food, uncontrolled blood sugar, obesity, smoking, consumption of alcohol, and inhaling of harmful gases. Earlier detection can reduce death rates and it also helps the doctors to give the proper treatment to the patients. The liver Cirrhosis datasets are analyzed by using Machine learning algorithms for accurate disease diagnosis. This paper proposed the four machine learning models such as SVM, Random Forest, Decision Tree, and Naive Bayes for analysis and prediction of liver cirrhosis. This work gathers 200 pictures of two separate classes, i.e., healthy liver, and unhealthy liver, using an image source. This image source is MedPix1, a free open-access archive of digital photographs that medical schools, medical practitioners, and academics can use. The methodology actually has a good result for the various classifiers but still can be improved. It is found that the Support vector machine and random forest classifiers give a 100% classification accuracy score followed by decision trees which are 84.61. The results achieved are relatively good when compared.