Classifying chief complaint in ear diseases using data mining techniques

Ears are the important organ for the hearing system. The system itself is very complicated. The clinicians attempt to determine the correct diagnosis using signs, symptoms and test results to formulate the hypothesis of the diagnosis before providing treatments. Most patients in this study have severe illness. Therefore, the clinicians decide to take the treatment by surgery rather than treating the patients with medicine. The result of the classification is very critical for the clinicians to support their diagnosis before giving the surgery to the patients. This study endeavors on using intelligent capability of data mining to discover hidden patterns in the data. Here, Artificial Neural Networks (ANN) and Naïve Bayes are utilized as techniques to classify patients with chief complaints in ear diseases. The results of classifying the ear diseases are very encouraging with the percentage accuracy of 100% for both techniques.