9786136524337 - medical diagnosis of cardiac disease using naïve bayes algorithm von babu, dr.domala kishore; roopa, y.mohana (5 Ergebnisse)
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Taschenbuch. Zustand: Neu. Medical Diagnosis of Cardiac Disease using Naïve Bayes Algorithm | Domala Kishore Babu (u. a.) | Taschenbuch | 56 S. | Englisch | 2018 | LAP LAMBERT Academic Publishing | EAN 9786136524337 | Verantwortliche Person für die EU: LAP Lambert Academic Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, cus…tomerservice[at]vdm-vsg[dot]de | Anbieter: preigu.
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In recent days the health industry has collected vast amounts of patient data, which unfortunately is not produced in order to give some hidden information, and thus to make effective decisions, which are connected with the base of th…e patient's data and are subjected to data mining. This work has developed a Decision Support in Heart Disease Prediction System (HDPS) using data mining modelling techniques namely Naïve Bayes and K-means, which are one of the most popular techniques. Using of medical data such as age, sex, blood pressure, blood sugar levels, chest pain etc. we can predict the likelihood of the patient. Most hospitals today use decision-support systems, but to get the results of the disease are largely limited i.e., Determine based on gender, marital status and who have been treated for heart failure. Solutions are always made in a hospital based on intuition and experience of doctors, and not on the rich knowledge data that are hidden in the database. This process leads to undesirable biases, errors and unnecessary health care costs, which affects the quality of services provided to patients.This will help make the diagnostic process more objective and more reliable. 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. Autor/Autorin: Babu Dr.Domala KishoreDr.D KISHORE BABU received Ph.D Doctorate in Computer Science and Engineering from JNTU,Kakinada Andhra Pradesh. He is currently working as professor in the department of computer sc…ience and engineering in Ins.
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Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -In recent days the health industry has collected vast amounts of patient data, which unfortunately is not produced in order to give some hidden information, and thus to make effective decisions, which are connected with the base of the pa…tient's data and are subjected to data mining. This work has developed a Decision Support in Heart Disease Prediction System (HDPS) using data mining modelling techniques namely Naïve Bayes and K-means, which are one of the most popular techniques. Using of medical data such as age, sex, blood pressure, blood sugar levels, chest pain etc. we can predict the likelihood of the patient. Most hospitals today use decision-support systems, but to get the results of the disease are largely limited i.e., Determine based on gender, marital status and who have been treated for heart failure. Solutions are always made in a hospital based on intuition and experience of doctors, and not on the rich knowledge data that are hidden in the database. This process leads to undesirable biases, errors and unnecessary health care costs, which affects the quality of services provided to patients.This will help make the diagnostic process more objective and more reliable.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 56 pp. Englisch.
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In recent days the health industry has collected vast amounts of patient data, which unfortunately is not produced in order to give some hidden information, and thus to make effective decisions, which are connected with the base of the pat…ient's data and are subjected to data mining. This work has developed a Decision Support in Heart Disease Prediction System (HDPS) using data mining modelling techniques namely Naïve Bayes and K-means, which are one of the most popular techniques. Using of medical data such as age, sex, blood pressure, blood sugar levels, chest pain etc. we can predict the likelihood of the patient. Most hospitals today use decision-support systems, but to get the results of the disease are largely limited i.e., Determine based on gender, marital status and who have been treated for heart failure. Solutions are always made in a hospital based on intuition and experience of doctors, and not on the rich knowledge data that are hidden in the database. This process leads to undesirable biases, errors and unnecessary health care costs, which affects the quality of services provided to patients.This will help make the diagnostic process more objective and more reliable.
