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Subject: search.filters.subject.diabetes mellitus

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    PublicationOpen Access
    Screening patients with tuberculosis for diabetes mellitus in Ampara, Sri Lanka
    (International Union Against Tuberculosis and Lung Disease, 2015-06-21) Rajapakshe, W; Isaakidis, P; Sagili, K. D; Kumar, A. M. V; Samaraweera, S; Pallewatta, N; Jayakody, W; Nissanka, A
    Given the well-known linkage between diabetes mellitus (DM) and tuberculosis (TB), the World Health Organization recommends bidirectional screening. Here we report the first screening effort of its kind from a chest clinic in the Ampara district of Sri Lanka. Of 112 TB patients registered between January 2013 and October 2014, eight had pre-existing DM. Of those remaining, 83 (80%) underwent fasting plasma glucose testing, of whom two (2%) and 17 (20%) were found to have diabetes and impaired fasting glucose, respectively. All of these were enrolled in care. Screening TB patients for DM was found to be feasible at the district level. Further studies at the provincial/country level are required before making any decision to scale up bidirectional screening.
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    Symptomatic Analysis Prediction of Kidney Related Diseases using Machine Learning
    (2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Lansakara, D.; Gunasekera, T.; Niroshana, C.; Weerasinghe, I.; Bandara, P.; Wijendra, D.
    Sri Lanka has been witnessing an increase in kidney disease issues for a while. Elderly kidney patients, kidney transplant patients who passed the risk level after the surgery are not treated in the emergency clinic. These patients are handed over to their families to take care of them. In any case, it is impossible to tackle a portion of the issues that emerge regarding the patient at home. It is hoped to enter patient’s data from home every day and to develop a system that can use that entered data to predict whether a patient is in an essential circumstance or not. Additionally, individuals in high-hazard regions cannot know whether they are in danger of creating kidney disappointments or not and individuals in danger of creating kidney sickness because of Diabetes Mellitus. Thus, we desire to emphasize the framework to improve answers for this issue. The research focuses on developing a system that includes early kidney disease prediction models involving machine learning classification algorithms by considering the relevant variables. In predictive analysis, six machine learning methods are used: Support Vector Machine (SVM with kernels), Random Forest (RF), Decision Tree, Logistic Regression, and Multilayer Perceptron. These classification algorithms' performance is evaluated using statistical measures such as sensitivity (recall), precision, accuracy, and F-score. In categorizing, accuracy determines which examples are accurate. The experimental results reveal that Support Vector Machine outperforms other classification algorithms in terms of accuracy.