<![CDATA[Autoimmune diseases occur when the immune system attacks the body’s own tissues, causing serious complications and overlapping symptoms that challenge early detection. This study reviews the use of the K-Nearest Neighbors (K-NN) algorithm for classifying autoimmune diseases through a systematic literature review of five articles. Compared to methods like Genetic Algorithms, Support Vector Machines (SVM), and Single Layer Perceptrons (SLP), K-NN shows high accuracy when optimal parameters and neighbor counts are used. However, challenges include sensitivity to imbalanced data and high computational demands for large datasets. Combining K-NN with optimization techniques, such as Genetic Algorithms, enhances accuracy and stability. The study concludes that K-NN is effective for classifying autoimmune diseases, especially with hybrid approaches, and recommends further research with larger datasets.]]>
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