Kernel perceptron algorithm for sinusitis classification

Sinusitis is one of the most commonly diagnosed diseases in the world. Its diagnosis is usually based on clinical signs and symptoms, which led to the development and use of many machine learning methods to provide a better diagnosis. This research, therefore, proposed a kernel perceptron method applied to the sinusitis dataset, consisting of 102 acute and 98 chronic samples, obtained from Cipto Mangunkusumo Hospital in Indonesia. This research utilized the RBF and polynomial kernel function for several k values in k-fold cross-validation and compared the results in accuracy, sensitivity, precision, specificity, and Fl-Score. From the experiments, it was concluded that the kernel parameter s = 0.0001 obtained excellent performance in every k-fold, with a better performance achieved using 10-fold cross-validation. Meanwhile, the polynomial degree did not affect the kernel perceptron performance. However, the use of 7-fold cross-validation can be considered to obtain better performance of kernel perceptron based on polynomial kernel.