NDLI: An exploratory study in classification methods for patients' dataset

Content Provider	IEEE Xplore Digital Library
Author	Mutalib, S. Ali, N.A. Rahman, S.A. Mohamed, A.
Copyright Year	2009
Description	Author affiliation: Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia (Mutalib, S.; Ali, N.A.; Rahman, S.A.; Mohamed, A.)
Abstract	There are various methods in data mining that can be applied in classification data. This paper discusses the experiments done in classifying ICU data. The dataset consists of 25 variables for 410 patients. The goal of this experiment is to determine the survival of the patients, so the targeted output are alive and dead. Three selected data mining methods are Decision Tree, Naives Bayes and Logistics Regression. Based on mean absolute error and root-squared error, the later method provides a better result. The result of classification could be used to help hospitals in predicting their patients' status and provide better way of antibiotic treatment. Applying an intelligent tool to classify the antibiotic resistance may support the decision making to diagnose the patients in an effective way. A right treatment will make sure the patient is survived. This intelligent tool for managing medicine dosage is worthy and brings a huge impact to medical sector.
Starting Page	79
Ending Page	83
File Size	66991
Page Count	5
File Format	PDF
ISBN	9781424449446
DOI	10.1109/DMO.2009.5341907
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-10-27
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Classifications Logistics Regression Decision making Medical treatment Data mining Hospitals Antibiotics Naives Bayes ICU Decision Trees Decision trees Medical diagnostic imaging Regression tree analysis Logistics Immune system
Content Type	Text
Resource Type	Article

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