Application of data mining in medical decision support systems

Habib Shariff Mahamud

ISBN 10: 6139920140 / ISBN 13: 9786139920143

Published by LAP LAMBERT Academic Publishing, 2019

About this Item

Application of data mining in medical decision support systems | Habib Shariff Mahamud | Taschenbuch | 84 S. | Englisch | 2019 | LAP LAMBERT Academic Publishing | EAN 9786139920143 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Seller Inventory # 117258497

Bibliographic Details

• Title: Application of data mining in medical decision support systems
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2019
• Language: English
• ISBN 10: 6139920140
• ISBN 13: 9786139920143
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 6D millimeters 220 millimeters width by 150 millimeters height by 6 millimeters depth
• Weight: 143 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

Medical decision support system (MDSS) are now being used in many health care institutions across the glove, these institutions have large amount of medical data stored in different format and may contain relevant data that are hidden. The use of data mining is to extract hidden knowledge from a relevant data, that is why the main aim of this book is to show how data mining methods can be applied in medical decision support system and also to design a web based expert system that can predict heart condition using neural network. The design of the system is based on VA Medical center long beach database and collected from the UCI machine learning repository. After analyzing several medical decision support systems in the relevant literature, three algorithms have been identified: multilayer perceptron, decision tree and Naïve Bayes. These algorithms are tested under different configuration in order to find the best on the two medical dataset. Thereafter, a comparison was made with respect to their performance based on some set of performance metrics. The analysis was done using WEKA on the two medical dataset which are diabetes and heart diseases database.