Data Mining Techniques and Medical Decision Making for Urological Dysfunction

View Sample PDF

Author(s): N. Sriraam (Multimedia University, Malaysia), V. Natasha (Multimedia University, Malaysia)and H. Kaur (Multimedia University, Malaysia)
Copyright: 2008
Pages: 11
Source title: Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-59904-951-9.ch153

Keywords: Data Mining and Databases / Data Warehousing / Information Science Reference / Library & Information Science

Purchase

View Data Mining Techniques and Medical Decision Making for Urological Dysfunction on the publisher's website for pricing and purchasing information.

Abstract

Data mining has been emerging recently as a viable computational tool for autonomous decision making especially in the field of medical applications. It has provided diagnostic solutions for skin and breast cancer detection, brain tumor detection, and also for other classification problems. In this chapter, we explore two data mining techniques, namely, association mining and decision tree mining, for predicting the life span of the kidney failure patients who have undergone routine dialysis. The total parameters used for this study were 28 attributes. The optimal prioritized parameters that decide the survival rate are reported and it can be concluded from the experimental results that the decision tree approach yields promising results.

The IRMA Community

Research IRM

Data Mining Techniques and Medical Decision Making for Urological Dysfunction

Purchase

Abstract

Related Content

IRMA Sponsors