Zustand: Como nuevo. : Este libro explora el uso de t�cnicas de miner�a de datos en estudios epidemiol�gicos de diabetes en Ir�n. Se centra en c�mo los algoritmos de miner�a de datos pueden ser implementados para predecir, clasificar y agrupar datos brutos en el paso de preprocesamiento, mejorando tanto la investigaci�n cient�fica como las pr�cticas reales. El libro revisa estudios y modelos iran�es de miner�a de datos diab�ticos, destacando m�todos para manejar valores faltantes en bases de datos de pacientes reales y las asociaciones valiosas obtenidas. EAN: 9786204212951 Tipo: Libros Categor�a: Tecnolog�a|Ciencias|Salud y Bienestar T�tulo: Iranian Epidemiological Diabetic Statistics Using Data Mining Techniques Autor: Alireza Naghsh| Seyed Hesamoddin Hashemi| Fatemehalsadat Beheshtinejad Editorial: LAP LAMBERT Academic Publishing Idioma: en P�ginas: 112 Formato: tapa blanda. Bestandsnummer des Verk�ufers Happ-2024-05-28-a88249f0

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Iranian Epidemiological Diabetic Statistics Using Data Mining Techniques

Alireza Naghsh|Seyed Hesamoddin Hashemi|Fatemehalsadat Beheshtinejad

Verlag: LAP LAMBERT Academic Publishing, 2021

ISBN 10: 6204212958 ISBN 13: 9786204212951

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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Naghsh AlirezaDr. Alireza Naghsh is an Assistant Professor at Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran. Seyed Hesamoddin Hashemi, Fatemehalsadat Beheshtinejad, and Ali Mojarrad . Bestandsnummer des Verk�ufers 522837793

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Iranian Epidemiological Diabetic Statistics Using Data Mining Techniques : Data Mining Techniques in Iranian Diabetic Studies

Alireza Naghsh

Verlag: LAP LAMBERT Academic Publishing, 2021

ISBN 10: 6204212958 ISBN 13: 9786204212951

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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Large medical databases require the knowledge-processed data mining to improve healthcare results. Diabetes, a global chronic disease, needs some rigid predictive tools based on the patient physiological and pathological profiles. Hence, the prevention programs can have beneficial consequences in order to avoid the final disaster. Data mining algorithms are implemented for predicting, classifying, and clustering raw data in the pre-processing step for further processing in both scientific research and real-life practices. The treatment decisions and early diagnosis of different diseases can be based on these results. The improved outcomes can be obtained through the integration of these algorithms, including the identified diabetic severity and its associated consequences, as well as lower healthcare costs for both patients and healthcare systems. In this book, Iranian diabetic data mining studies and models are reviewed with some obtained valuable associations. The important contribution is the diverse methods introduced in order to handle missing values in real patient databases, but with many challenges in diabetic diagnosis and some research gaps remained for future studies. Bestandsnummer des Verk�ufers 9786204212951

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