Abstract
Urinary tract infection (UTI) is a disease related to infection of the urethra, bladder, ureters, or the kidneys. That comprises the urinary tract caused by pathogenic organisms such as bacteria, fungi, and parasites. Diagnosing the UTI in preliminary stages requires experts in a related field where it can be detected clinically. This paper presents the configuration evaluation by the k-nearest neighbors (KNN) approach, that is, to find the best value of k that classifies with the highest accuracy. The approach is then used to build a simple application with a user interface for the diagnosis of UTI disease, whether cystitis, pyelonephritis, both, or no infection. Moreover, the application is implemented using the JAVA programming language, and the diagnosis is established based on six symptoms. That is, the temperature of the patient, occurrence of nausea, lumbar pain, urine pushing, micturition pains and burning, itch or swelling of urethra outlet which affected the patient. The result indicates that the algorithm performed best with 97.4% accuracy when applying the suggested value of k = 6. This also gives insight into the performance of an application developed, with the ability to classify UTI-related disease correctly.
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Jamaluddin, M.N.F. et al. (2020). An Application of Presumptive Diagnosis for Urinary Tract Infection via kNN Algorithm Approach. In: Alias, N., Yusof, R. (eds) Charting the Sustainable Future of ASEAN in Science and Technology . Springer, Singapore. https://doi.org/10.1007/978-981-15-3434-8_32
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DOI: https://doi.org/10.1007/978-981-15-3434-8_32
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