Implementation Learning Vector Quantization Using Neural Network for Classification of Ear, Nose and Throat Disease

Abstract

Abstract This study aims to use data from 57 patients at Rantauprapat Hospital to train a Neural Network using a quantization learning vector method for the categorization of ear, nose, and throat disorders. The input factors were fever, tiredness, nausea, breathing pain, sore throat, hearing loss, allergies, chills and sweating, and thick and transparent mucus. The factors studied were ear canal infections, pharyngitis of the neck, throat, nose, and sinusitis. The findings revealed that ten neurons with an objective value of 0.01 in the learning rate range of 0.01 - 0.05 resulted in categorizing snoring, nose, and ear disorders, including the input layer. The MATLAB program is utilized in this approach, with an average accuracy of 67 per cent and a mean square error of 0.2.