Title of article :
Bayesian Network Classification of Gastrointestinal Bleeding
Author/Authors :
Aisha, Nazziwa Universiti Putra Malaysia - Faculty of Science - Department of Mathematics, Malaysia , Adam, Mohd Bakri Universiti Putra Malaysia - Intitute for Mathematical Research, Malaysia , Shohaimi, Shamarina Universiti Putra Malaysia - Faculty of Science - Department of Biology, Malaysia , Mustapha, Aida Universiti Putra Malaysia - Faculty of Science - Department of Computer Science, Malaysia
Abstract :
The source of gastrointestinal bleeding (GIB) remains uncertain in patients presenting without hematemesis. This paper aims at studying the accuracy, specificity and sensitivity of the Naive Bayesian Classifier (NBC) in identifying the source of GIB in the absence of hematemesis. Data of 325 patients admitted via the emergency department (ED) for GIB without hematemesis and who underwent confirmatory testing were analysed. Six attributes related to demography and their presenting signs were chosen. NBC was used to calculate the conditional probability of an individual being assigned to Upper Gastrointestinal bleeding (UGIB) or Lower Gastrointestinal bleeding (LGIB). High classification accuracy (87.3 %), specificity (0.85) and sensitivity (0.88) were achieved. NBC is a useful tool to support the identification of the source of gastrointestinal bleeding in patients without hematemesis.
Keywords :
Bayesian network classifiers , Emergency department , hematemesis , upper gastrointestinal bleeding , Naive Bayes classifier , lower gastrointestinal bleeding , data mining
Journal title :
Pertanika Journal of Science and Technology ( JST)
Journal title :
Pertanika Journal of Science and Technology ( JST)
