Implementation of Fuzzy K-Nearest Neighbor Method in Dengue Disiase Classification
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DOI:
https://doi.org/10.56211/hanif.v1i2.14Keywords:
Dengue Fever; Classification; FKNN; K-Fold Cross Validatio; Confusion Matrixclustering
Abstract
Dengue hemorrhagic fever (DHF) is a condition brought on by infection with the dengue virus. DHF is a severe illness with hemorrhagic clinical signs that can result in shock and death. One of the four viral serotypes of the genus Flavivirus is responsible for DHF. DHF symptoms include fever, joint pain, red skin patches, and others that are similar to those of other illnesses. So that there are no errors in illness prediction, strong accuracy and accuracy are required when classifying DHF patients or not. The Fuzzy K-Nearest Neighbor (FKNN) method is used in this study to classify dengue sickness in order to obtain the best classification outcomes. In this investigation, k was searched for eight times, with values of 3,5,7,9,11,13,15, and 20. Each K's accuracy statistics are 75.15, 75.16, 77.58%, 79.51%, 85.01%, 78.14%, and 75.20 percent. K = 13, which has an accuracy score of 85.01%, yields the highest accuracy.
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