Browsing by Author "Shamarina, Shohaimi"

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    Bayesian Network Classification of Gastrointestinal Bleeding
    (Universiti Putra Malaysia Press (Pertanika Journal of Science & Technology), 2014) Nazziwa, Aisha; Mohd, Bakri Adam; Shamarina, Shohaimi; Aida, Mustapha
    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
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    Classification models for predicting the source of gastrointestinal bleeding in the absence of hematemesis
    (Basic Research Journal of Medicine and Clinical Sciences, 2013-08) Nazziwa, Aisha; Mohd, Bakri Adam; Shamarina, Shohaimi
    Management of acute gastrointestinal bleeding necessitates the identification of the source of bleed. The source of bleeding which is clear in patients presenting with hematemesis, is unclear in the absence of it. Logistic regression, decision tree, naïve Bayes, LogitBoost and KNN models were constructed from non endoscopic data of 325 patients admitted via the emergence department (ED) for GIB without hematemesis. The performance of the models in predicting the source of bleeding into upper gastrointestinal bleeding or lower gastrointestinal bleeding was compared. Overall the models demonstrate good performance with regards to sensitivity specificity, PPV, NPV and classification accuracy on the simulated data. On the GIB data, the naive Bayes model performed best with a prediction accuracy and sensitivity of 86%, specificity of 85% and area under curve of 92%. Classification models can help to predict the source of gastrointestinal bleeding for patients presenting without hematemesis and may generally be useful in decision support in the ED. The models should be explored further for clinical relevance in other settings
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    Comparison of the Naive Bayes Classifier and Instance Based Learner in Classifying Upper Gastrointestinal Bleeding
    (MATEMATIKA, 2013) Naziwa, Aisha; Mohd, Bakri Adam; Shamarina, Shohaimi
    Upper gastrointestinal bleeding is a medical emergence that results in high medical costs and death. Management of this disease requires ascertaining the cause of bleeding. The cause of bleeding is classified into esophageal and gastric causes. Based on health survey data, this study compares the accuracy of the naive Bayes classifier and an instance based learner in the classification of the cause of bleeding. The two classifiers are learned and trained on data collected from patients admitted for upper gastrointestinal bleeding. The naive Bayes classifier achieves a classification accuracy of 71% accuracy compared to 68% of the instance based learner
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    Effect of Missing Value Methods on Bayesian Network Classification of Hepatitis Data
    (International Journal of Computer Science and Telecommunication, 2013-06) Naziwa, Aisha; Mohd, Bakri Adam; Shamarina, Shohaimi
    Missing value imputation methods are widely used in solving missing value problems during statistical analysis. For classification tasks, these imputation methods can affect the accuracy of the Bayesian network classifiers. This paper study’s the effect of missing value treatment on the prediction accuracy of four Bayesian network classifiers used to predict death in acute chronic Hepatitis patients. Missing data was imputed using nine methods which include, replacing with most common attribute, support vector machine imputation (SVMI), K-nearest neighbor (KNNI), Fuzzy K-means Clustering (FKMI), K-means Clustering Imputation (KMI), Weighted imputation with K-Nearest Neighbor (WKNNI), regularized expectation maximization (EM), singular value decomposition (SVDI), and local least squares imputation (LLSI). The classification accuracy of the naive Bayes (NB), tree augmented naive Bayes (TAN), boosted augmented naive Bayes (BAN) and general Bayes network classifiers (GBN) were recorded. The SVMI and LLSI methods improved the classification accuracy of the classifiers. The method of ignoring missing values was better than seven of the imputation methods. Among the classifiers, the TAN achieved the best average classification accuracy of 86.3% followed by BAN with 85.1%.