Classification of Autoimmune Diseases Using the K-Nearest Neighbors Algorithm

Abstract

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.