On the use of Bayesian network classifiers to classify patients with peptic ulcer among upper gastrointestinal bleeding patients

A Bayesian network classifier is one type of graphical probabilistic models that is capable of representing relationship between variables in a given domain under study. We consider the naive Bayes, tree augmented naive Bayes (TAN) and boosted augmented naive Bayes (BAN) to classify patients with peptic ulcer disease among upper gastro intestinal bleeding patients. We compare their performance with IBk and C4.5. To identify relevant variables for peptic ulcer disease, we use some methodologies for attributes subset selection. Results show that, blood urea nitrogen, hemoglobin and gastric malignancy are important for classification. BAN achieves the best accuracy of 77.3 and AUC of (0.81) followed by TAN with 72.4 and 0.76 respectively among Bayesian classifiers. While the accuracy of the TAN is improved with attribute selection, the BAN and IBK are better off without attribute selection

Classification models for predicting the source of gastrointestinal bleeding in the absence of hematemesis

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

Bayesian network modelling of upper gastrointestinal bleeding

Bayesian networks are graphical probabilistic models that represent causal and other relationships between domain variables. In the context of medical decision making, these models have been explored to help in medical diagnosis and prognosis. In this paper, we discuss the Bayesian network formalism in building medical support systems and we learn a tree augmented naive Bayes Network (TAN) from gastrointestinal bleeding data. The accuracy of the TAN in classifying the source of gastrointestinal bleeding into upper or lower source is obtained. The TAN achieves a high classification accuracy of 86% and an area under curve of 92%. A sensitivity analysis of the model shows relatively high levels of entropy reduction for color of the stool, history of gastrointestinal bleeding, consistency and the ratio of blood urea nitrogen to creatinine. The TAN facilitates the identification of the source of GIB and requires further validation

Effect of missing value methods on Bayesian network classification of hepatitis data

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%

Comparison of the naive Bayes classifier and instance based learner in classifying upper gastrointestinal bleeding

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

Bayesian network classification of gastrointestinal bleeding

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

Preparedness, Identification and Care of COVID-19 Cases by Front Line Health Workers in Selected Health Facilities in Mbale District Uganda: A Cross-Sectional Study

Introduction: The nature of work of Health care professionals exposes them to high risks of contracting COVID-19 and spreading it among themselves, to their patients and subsequently to the general community. Thus, it is essential that frontline health workers are equipped with both material and knowledge to enable them accurately suspect, detect, isolate, and manage COVID-19 cases. Findings have indicated a high prevalence of COVID-19 infections among front-line health workers. The Current Study assessed preparedness, identification, and care of COVID-19 Cases by frontline health workers in selected health facilities in Mbale District.Methodology: Across sectional survey was used to collect quantitative data using Google forms, An online platform for data collection. Data was collected from 189 frontline health workers in both government and private Health facilities in Mbale District between April and August 2020. Data was analysed using Statistical Package for the Social Sciences (SPSS) version 20.Findings: The study found that a good proportion of frontline health workers can identify cases by symptom and case definitions as probable case 113/189(59.8%), suspected case 60/189(36%) and confirmed case 22/189 (11.6%).There were generally low levels of preparedness in terms of initial service care being offered with the highest being 53/189(28.2%) and 50/189(26.4%) for facilities that had places for isolation and those with intravenous fluids respectively and the least was being able to offer oxygen and Intensive Care Services at 43/189(22.0%) and 20/189(10.3%) respectively.Conclusion and recommendations: There’s a need to ensure a continuous supply of PPEs and IPC materials to health facilities. CPD programs are essential in equipping Health workers with up-to-date information on COVID-19 Case Management. Facilities should be supported to setup isolation facilities at all levels, both permanent and temporary. Provision of Face masks to health workers should be prioritised and hand washing facilities should be installed at every serving point

Assessing Knowledge and Practices of the Community towards Corona Virus Disease 2019 in Mbale Municipality, Uganda: Across Section Study

Background: The Corona virus disease, first identified in Wuhan city, Hubei province of China, is a respiratory illness caused by Novel  Corona Virus also known as Severe Acute Respiratory Syndrome Corona Virus 2 (SARS Cov.2). The disease is characterised by; dry cough and shortness of breath with difficulty in breathing and at least 2 of the following; fever, chills, muscle pain, headache, sore throat and loss of test and smell. Uganda in general and Mbale in particular has people of diverse culture, religion and ethnic background as well as diverse socio economic activities with various practices. This multi-cultural environment creates differences in perception of information and practices. Most cultures encourage socialisation through social functions like attending weddings, funerals, work places and  gatherings and Muslims who have to go for congregation prayers in the mosques 5 times a day among others. This puts such communities at risk of spreading the disease very fast and slow in adapting to control measures Aim: In this study, we aimed at assessing knowledge and practices of the community towards COVID 19 in Mbale municipality. Methods and Materials: A cross section study was used; Data was obtained using a Questionnaires to a sample of 355 respondents and an observation tool was also used to observe behaviour patterns and practices of 776 participants towards the control measures of COVID-19. Results: There was a total of 355 respondents with 208 /355 (58.59%) male and 147/355 (41.4%) female. 149/355(42%) possessed good  knowledge, 131/355(36.9%) had moderate knowledge and 75/355(21%) had a little knowledge on COVID-19. Participants who were single and aged between 21-30 years were found to be more knowledgeable than other groups (P value=.001 and P value=.003 respectively).The source of COVID 19 information was mainly from television and radios 124/248 (50%) and social media 34/248 (21.8%) and the least source of information being 14/248(5.6%) and 9/248(3.6%) from health workers and Religious leaders respectively. 496/776 (64%) of the  respondents observed, washed their hands and only124/776 (16%) of the respondents wore face masks. 98/776 (12.6%) were seen shaking hands and 15/776(2%) were seen hugging. Conclusion: Use of appropriate and well-designed Health education materials on radios, televisions and social media platforms like Facebook and twitter among others can be effective means of communication since they can reach the highest number of people. Ministry of Health should design ways for systematically integrating both political and religious leaders in Health Education Campaigns.  Government should provide facemasks and enforce their use. A study to assess the ability of both political and religious leaders in health promotion campaigns should be carried out

COVID-19 Vaccination: Prevalence and Associated Factors among Students and Staff (A Case of Islamic University in Uganda)

Background: COVID-19 Vaccination is an important control measure for the spread of covid -19 with in Academic Institutions. This study aimed to investigated the Prevalence of COVID-19 Vaccination and associated factors among University Students and staff. Subjects and Method: This was a cross-sectional study conducted at Islamic University, Uganda, from July to October 2021. A number of 397 students and staff of IUIU were selected purposively. The dependent variable was vaccination status. The independent variables were age, gender, education status, source of income, religion, marital status, nationality risk perception. Data were collected using an online google form sent via emails WhatsApp and ERP and analyze using Chi-square. Results: There were 397 participants, the modal age was 16-25 years 233 (58.7%) were male, the prevalence of COVID-19 Vaccination was 20.4 % (81). Factors such as age (OR= 0.59; 95% CI= 0.25 to 1.37; p<0.001), Gender (OR= 0.59; 95% CI= 1.06 to 3.00; p=0.026), marital status (OR= 1.55; 95% CI= 0.20 to 0.56; p<0.001) were associated with uptake of COVID-19 vaccination. Conclusion: The Study found a low Prevalence of COVID-19 Vaccination (20.4%) among students and staff at IUIU, and a number of factors presented above were responsible for this. More Education and sensitization on the importance of Vaccination is still needed. A follow up study on the same should be done after full opening of academic institutions. Keywords: prevalence, COVID-19, vaccination, factor, studend and staff, Uganda Correspondence: Naziru Rashid. University Medical Officer. Islamic University in Uganda (IUIU). Soroti, Mbale Rd, Mbale, Uganda. Email: [email protected]. Mobile phone: +256702038 741 Journal of Health Promotion and Behavior (2022), 07(01): 18-27 DOI: https://doi.org/10.26911/thejhpb.2021.07.01.0

Bayesian network modeling of gastrointestinal bleeding

Acute gastrointestinal bleeding (GIB) is a common medical emergency with 50-150 per 100,000 people admitted per year. Although 80 percent of GIB cases stop spontaneously, it is important to determine the source of bleeding and establish adiagnosis such that possible recurrences are prevented and that the most suitable management may be given in future episodes. In the emergency room, when a patient shows signs of hematemesis (vomiting of red blood), it is obvious that the patient has upper gastrointestinal bleeding. In the absence of hematemesis however,the source of bleeding remains unclear. While the diagnosis of GIB is best done by a gastroenterologist, it is not always feasible, due to scarcity of resources and time. A reliable classification model would be very helpful in diagnosing patients more efficiently and effectively targeting the scarce resources.Current review of the literature, did not reveal any model that predicts the source of GIB in the absence of hematemesis. This thesis uses a graphical modeling approach,specialcally Bayesian networks, to model the different outcomes of GIB. One key advantage of Bayesian network models in this context is their ability to predict the outcome with partial observations on variables or attributes. The four outcome variables predicted are: source of bleeding, need for urgent blood resuscitation,need for urgent endoscopy, and disposition. Performance of the models is assessed by classification or prediction accuracy, area under curves, sensitivity and specificity values. The Bayesian network models provide good accuracy for the prediction of the source of bleeding and need for urgent blood resuscitation but did not do well on predicting need for urgent endoscopy, and disposition. The models require further validation if they are to be used in clinical settings