Erythemato-Squamous Diseases Diagnosis Based on Support Vector Machine Optimized by Black-Winged Kite Algorithm

In response to the difficulty in distinguishing different types of erythemato-squamous diseases, this paper uses support vector machine (SVM) to construct a diagnostic classifier for erythemato-squamous diseases, and optimizes SVM classifier hyperparameters by using black-winged kite algorithm (BKA). Finally, the BKA-SVM classifier for diagnosing erythemato-squamous diseases is established. To test the effectiveness of the classifier, a comparative experiment was conducted with two SVM classifiers optimized by tuna swarm algorithm (TSO) and Osprey optimization algorithm (OOA) respectively. The experimental result indicates that the BKA-SVM classifier has higher accuracy rate in the diagnosis of erythemato-squamous diseases.